library(ggplot2)
library(dplyr)
library(janitor)
library(tidyr)
library(corrplot)
library(GGally)
library(readr)
library(ROSE)
library(caret)
library(randomForest)
library(knitr)
library(pROC)
library(reticulate)
use_virtualenv("tf_now", required = TRUE)
library(keras)
library(tensorflow)
library(caret)
library(DMwR)
library(kableExtra)
library(tibble)
library(rmdformats)
library(prettydoc)
library(hrbrthemes)
library(tint)
library(tufte)
# Load the dataset from the URL
url <- "https://raw.githubusercontent.com/NikoletaEm/datasps/refs/heads/main/heart_attack_prediction_dataset.csv"
data <- read.csv(url)
# View the first few rows of the dataset
head(data)
## Patient.ID Age Sex Cholesterol Blood.Pressure Heart.Rate Diabetes
## 1 BMW7812 67 Male 208 158/88 72 0
## 2 CZE1114 21 Male 389 165/93 98 1
## 3 BNI9906 21 Female 324 174/99 72 1
## 4 JLN3497 84 Male 383 163/100 73 1
## 5 GFO8847 66 Male 318 91/88 93 1
## 6 ZOO7941 54 Female 297 172/86 48 1
## Family.History Smoking Obesity Alcohol.Consumption Exercise.Hours.Per.Week
## 1 0 1 0 0 4.168189
## 2 1 1 1 1 1.813242
## 3 0 0 0 0 2.078353
## 4 1 1 0 1 9.828130
## 5 1 1 1 0 5.804299
## 6 1 1 0 1 0.625008
## Diet Previous.Heart.Problems Medication.Use Stress.Level
## 1 Average 0 0 9
## 2 Unhealthy 1 0 1
## 3 Healthy 1 1 9
## 4 Average 1 0 9
## 5 Unhealthy 1 0 6
## 6 Unhealthy 1 1 2
## Sedentary.Hours.Per.Day Income BMI Triglycerides
## 1 6.615001 261404 31.25123 286
## 2 4.963459 285768 27.19497 235
## 3 9.463426 235282 28.17657 587
## 4 7.648981 125640 36.46470 378
## 5 1.514821 160555 21.80914 231
## 6 7.798752 241339 20.14684 795
## Physical.Activity.Days.Per.Week Sleep.Hours.Per.Day Country Continent
## 1 0 6 Argentina South America
## 2 1 7 Canada North America
## 3 4 4 France Europe
## 4 3 4 Canada North America
## 5 1 5 Thailand Asia
## 6 5 10 Germany Europe
## Hemisphere Heart.Attack.Risk
## 1 Southern Hemisphere 0
## 2 Northern Hemisphere 0
## 3 Northern Hemisphere 0
## 4 Northern Hemisphere 0
## 5 Northern Hemisphere 0
## 6 Northern Hemisphere 1
## Patient.ID Age Sex Cholesterol
## Length:8763 Min. :18.00 Length:8763 Min. :120.0
## Class :character 1st Qu.:35.00 Class :character 1st Qu.:192.0
## Mode :character Median :54.00 Mode :character Median :259.0
## Mean :53.71 Mean :259.9
## 3rd Qu.:72.00 3rd Qu.:330.0
## Max. :90.00 Max. :400.0
## Blood.Pressure Heart.Rate Diabetes Family.History
## Length:8763 Min. : 40.00 Min. :0.0000 Min. :0.000
## Class :character 1st Qu.: 57.00 1st Qu.:0.0000 1st Qu.:0.000
## Mode :character Median : 75.00 Median :1.0000 Median :0.000
## Mean : 75.02 Mean :0.6523 Mean :0.493
## 3rd Qu.: 93.00 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :110.00 Max. :1.0000 Max. :1.000
## Smoking Obesity Alcohol.Consumption Exercise.Hours.Per.Week
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.002442
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 4.981579
## Median :1.0000 Median :1.0000 Median :1.0000 Median :10.069559
## Mean :0.8968 Mean :0.5014 Mean :0.5981 Mean :10.014284
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:15.050018
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :19.998709
## Diet Previous.Heart.Problems Medication.Use Stress.Level
## Length:8763 Min. :0.0000 Min. :0.0000 Min. : 1.00
## Class :character 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 3.00
## Mode :character Median :0.0000 Median :0.0000 Median : 5.00
## Mean :0.4958 Mean :0.4983 Mean : 5.47
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 8.00
## Max. :1.0000 Max. :1.0000 Max. :10.00
## Sedentary.Hours.Per.Day Income BMI Triglycerides
## Min. : 0.001263 Min. : 20062 Min. :18.00 Min. : 30.0
## 1st Qu.: 2.998794 1st Qu.: 88310 1st Qu.:23.42 1st Qu.:225.5
## Median : 5.933622 Median :157866 Median :28.77 Median :417.0
## Mean : 5.993690 Mean :158263 Mean :28.89 Mean :417.7
## 3rd Qu.: 9.019124 3rd Qu.:227749 3rd Qu.:34.32 3rd Qu.:612.0
## Max. :11.999313 Max. :299954 Max. :40.00 Max. :800.0
## Physical.Activity.Days.Per.Week Sleep.Hours.Per.Day Country
## Min. :0.00 Min. : 4.000 Length:8763
## 1st Qu.:2.00 1st Qu.: 5.000 Class :character
## Median :3.00 Median : 7.000 Mode :character
## Mean :3.49 Mean : 7.024
## 3rd Qu.:5.00 3rd Qu.: 9.000
## Max. :7.00 Max. :10.000
## Continent Hemisphere Heart.Attack.Risk
## Length:8763 Length:8763 Min. :0.0000
## Class :character Class :character 1st Qu.:0.0000
## Mode :character Mode :character Median :0.0000
## Mean :0.3582
## 3rd Qu.:1.0000
## Max. :1.0000
# Clean the column names
data <- clean_names(data)
# Remove variables
data <- select(data, -patient_id)
data <- select(data, -income)
data <- select(data, -country)
data <- select(data, -hemisphere)
# View the cleaned names
names(data)
## [1] "age" "sex"
## [3] "cholesterol" "blood_pressure"
## [5] "heart_rate" "diabetes"
## [7] "family_history" "smoking"
## [9] "obesity" "alcohol_consumption"
## [11] "exercise_hours_per_week" "diet"
## [13] "previous_heart_problems" "medication_use"
## [15] "stress_level" "sedentary_hours_per_day"
## [17] "bmi" "triglycerides"
## [19] "physical_activity_days_per_week" "sleep_hours_per_day"
## [21] "continent" "heart_attack_risk"
data <- data %>%
mutate(across(where(~ all(na.omit(.) %in% c(0, 1))), ~ factor(., labels = c("no", "yes"))))
str(data)
## 'data.frame': 8763 obs. of 22 variables:
## $ age : int 67 21 21 84 66 54 90 84 20 43 ...
## $ sex : chr "Male" "Male" "Female" "Male" ...
## $ cholesterol : int 208 389 324 383 318 297 358 220 145 248 ...
## $ blood_pressure : chr "158/88" "165/93" "174/99" "163/100" ...
## $ heart_rate : int 72 98 72 73 93 48 84 107 68 55 ...
## $ diabetes : Factor w/ 2 levels "no","yes": 1 2 2 2 2 2 1 1 2 1 ...
## $ family_history : Factor w/ 2 levels "no","yes": 1 2 1 2 2 2 1 1 1 2 ...
## $ smoking : Factor w/ 2 levels "no","yes": 2 2 1 2 2 2 2 2 2 2 ...
## $ obesity : Factor w/ 2 levels "no","yes": 1 2 1 1 2 1 1 2 2 2 ...
## $ alcohol_consumption : Factor w/ 2 levels "no","yes": 1 2 1 2 1 2 2 2 1 2 ...
## $ exercise_hours_per_week : num 4.17 1.81 2.08 9.83 5.8 ...
## $ diet : chr "Average" "Unhealthy" "Healthy" "Average" ...
## $ previous_heart_problems : Factor w/ 2 levels "no","yes": 1 2 2 2 2 2 1 1 1 1 ...
## $ medication_use : Factor w/ 2 levels "no","yes": 1 1 2 1 1 2 1 2 1 1 ...
## $ stress_level : int 9 1 9 9 6 2 7 4 5 4 ...
## $ sedentary_hours_per_day : num 6.62 4.96 9.46 7.65 1.51 ...
## $ bmi : num 31.3 27.2 28.2 36.5 21.8 ...
## $ triglycerides : int 286 235 587 378 231 795 284 370 790 232 ...
## $ physical_activity_days_per_week: int 0 1 4 3 1 5 4 6 7 7 ...
## $ sleep_hours_per_day : int 6 7 4 4 5 10 10 7 4 7 ...
## $ continent : chr "South America" "North America" "Europe" "North America" ...
## $ heart_attack_risk : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 2 2 1 1 ...
# Data Tidying
data <- data %>%
mutate(
stress_level = case_when(
stress_level %in% 1:3 ~ "low",
stress_level %in% 4:7 ~ "moderate",
stress_level %in% 8:10 ~ "high",
TRUE ~ NA_character_
),
stress_level = factor(stress_level, levels = c("low", "moderate", "high"))
)
table(data$stress_level, useNA = "ifany")
##
## low moderate high
## 2646 3528 2589
data <- data %>%
separate(blood_pressure, into = c("systolic", "diastolic"), sep = "/", convert = TRUE)
# Classify into 4 blood pressure categories
data <- data %>%
mutate(
bp_category = case_when(
systolic >= 180 | diastolic >= 120 ~ "Hypertensive Crisis",
systolic >= 130 | diastolic >= 80 ~ "Hypertension",
systolic >= 120 & systolic < 130 & diastolic < 80 ~ "Elevated",
systolic < 120 & diastolic < 80 ~ "Normal",
TRUE ~ NA_character_
),
bp_category = factor(bp_category, levels = c("Normal", "Elevated", "Hypertension", "Hypertensive Crisis"))
)
data <- data %>%
mutate(
triglyceride_category = case_when(
triglycerides < 150 ~ "Normal",
triglycerides >= 150 & triglycerides <= 499 ~ "High",
triglycerides >= 500 ~ "Very High",
TRUE ~ NA_character_
),
triglyceride_category = factor(
triglyceride_category,
levels = c("Normal", "High", "Very High")
)
)
table(data$bp_category, useNA = "ifany")
##
## Normal Elevated Hypertension Hypertensive Crisis
## 1163 353 7149 98
table(data$triglyceride_category, useNA = "ifany")
##
## Normal High Very High
## 1358 3970 3435
data <- select(data, -triglycerides)
data <- data %>%
mutate(
bmi = case_when(
bmi < 18.5 ~ "Underweight",
bmi >= 18.5 & bmi < 25 ~ "Normal",
bmi >= 25 & bmi < 30 ~ "Overweight",
bmi >= 30 ~ "Obese",
TRUE ~ NA_character_
),
bmi = factor(
bmi,
levels = c("Underweight", "Normal", "Overweight","Obese")
)
)
data <- select(data, -obesity)
table(data$bmi, useNA = "ifany")
##
## Underweight Normal Overweight Obese
## 204 2619 2059 3881
data <- data %>%
mutate(
cholesterol = case_when(
cholesterol < 200 ~ "Normal",
cholesterol >= 200 & cholesterol < 240 ~ "Borderline High",
cholesterol >= 240 ~ "High",
TRUE ~ NA_character_
),
cholesterol = factor(
cholesterol,
levels = c("Normal", "Borderline High", "High")
)
)
table(data$cholesterol, useNA = "ifany")
##
## Normal Borderline High High
## 2452 1257 5054
# Generating New Variables
data <- data %>%
mutate(
high_risk_cardiometabolic = ifelse(
bp_category %in% c("Hypertension", "Hypertensive Crisis") &
bmi %in% c("Overweight", "Obese") &
cholesterol == "High",
1, 0
)
)
data <- data %>%
mutate(
stress_bp_combo = ifelse(
stress_level == "high" & bp_category %in% c("Hypertension", "Hypertensive Crisis"),
1, 0
)
)
data <- data %>%
mutate(across(where(~ is.integer(.) && !is.factor(.) && !is.character(.)), as.numeric))
numeric_vars <- data %>% select(where(is.numeric))
# Histograms
par(mfrow = c(2, 2))
for (var in names(numeric_vars)[1:8]) {
hist(numeric_vars[[var]], main = paste("Histogram of", var), xlab = var, col = "skyblue", border = "white")
}
# Boxplots
par(mfrow = c(2, 2))
for (var in names(numeric_vars)[1:8]) {
boxplot(numeric_vars[[var]], main = paste("Boxplot of", var), col = "orange")
}
target_var <- "heart_attack_risk"
plot_vars <- c("sex","family_history" ,"diabetes","smoking","alcohol_consumption", "bmi", "cholesterol", "stress_level", "bp_category", "triglyceride_category")
# Define color palettes
colors_2 <- c("lightblue", "salmon")
colors_3 <- c("lightblue", "salmon", "palegreen")
colors_4 <- c("lightblue", "salmon", "palegreen", "plum")
# Generate stacked bar plots with actual counts
for (var in plot_vars) {
levels_count <- n_distinct(data[[var]])
fill_colors <- switch(as.character(levels_count),
"2" = colors_2,
"3" = colors_3,
"4" = colors_4,
colors_4) # fallback
p <- ggplot(data, aes_string(x = target_var, fill = var)) +
geom_bar(position = "stack") + # Actual counts
scale_fill_manual(values = fill_colors) +
labs(
title = paste("Count of", var, "within heart attack risk groups"),
x = "Heart Attack Risk",
y = "Count",
fill = var
) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, size = 14))
print(p)
}
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Only numeric + target
ggpairs(data,
columns = which(sapply(data, is.numeric))[1:5], # adjust number of vars
aes(color = heart_attack_risk, alpha = 0.4))
# Correlation matrix
cor_matrix <- cor(numeric_vars, use = "complete.obs")
corrplot(cor_matrix,
method = "color",
type = "lower",
order = "hclust",
tl.col = "black",
tl.srt = 45,
tl.cex = 0.5,
addCoef.col = "black",
number.cex = 0.5,
diag = FALSE)
## age sex
## 0 0
## cholesterol systolic
## 0 0
## diastolic heart_rate
## 0 0
## diabetes family_history
## 0 0
## smoking alcohol_consumption
## 0 0
## exercise_hours_per_week diet
## 0 0
## previous_heart_problems medication_use
## 0 0
## stress_level sedentary_hours_per_day
## 0 0
## bmi physical_activity_days_per_week
## 0 0
## sleep_hours_per_day continent
## 0 0
## heart_attack_risk bp_category
## 0 0
## triglyceride_category high_risk_cardiometabolic
## 0 0
## stress_bp_combo
## 0
# Exercise Hours vs Heart Attack Risk
ggplot(data, aes(x = exercise_hours_per_week, fill = heart_attack_risk)) +
geom_histogram(position = "identity", alpha = 0.5, bins = 30) +
theme_minimal() +
labs(title = "Exercise Hours vs Heart Attack Risk")
# Heart Attack Risk by Continent
ggplot(data, aes(x = continent, fill = heart_attack_risk)) +
geom_bar(position = "fill") +
labs(title = "Heart Attack Risk by Continent", y = "Proportion") +
theme_minimal()
# North America-slightly more heart attack risk
# Check class balance
table(data$heart_attack_risk)
##
## no yes
## 5624 3139
# Handling Class Imbalance
data <- data %>%
mutate(across(where(is.character), as.factor))
data$heart_attack_risk <- as.factor(data$heart_attack_risk)
# Apply ROSE sampling
rose_data <- ROSE(heart_attack_risk ~ ., data = data, seed = 105)$data
# Check class balance again
table(rose_data$heart_attack_risk)
##
## no yes
## 4366 4397
set.seed(105) # for reproducibility
train_index <- createDataPartition(rose_data$heart_attack_risk, p = 0.8, list = FALSE)
train_data <- rose_data[train_index, ]
test_data <- rose_data[-train_index, ]
colnames(train_data)
## [1] "age" "sex"
## [3] "cholesterol" "systolic"
## [5] "diastolic" "heart_rate"
## [7] "diabetes" "family_history"
## [9] "smoking" "alcohol_consumption"
## [11] "exercise_hours_per_week" "diet"
## [13] "previous_heart_problems" "medication_use"
## [15] "stress_level" "sedentary_hours_per_day"
## [17] "bmi" "physical_activity_days_per_week"
## [19] "sleep_hours_per_day" "continent"
## [21] "heart_attack_risk" "bp_category"
## [23] "triglyceride_category" "high_risk_cardiometabolic"
## [25] "stress_bp_combo"
Random Forest
metrics_rfs <- data.frame(
Model = character(),
Accuracy = numeric(),
Precision = numeric(),
Recall = numeric(),
F1 = numeric(),
stringsAsFactors = FALSE
)
Random Forest 1
# Random Forest Base Model
set.seed(105)
rf_model <- randomForest(
heart_attack_risk ~ .,
data = train_data,
ntree = 500, # number of trees
mtry = floor(sqrt(ncol(train_data) - 1)),
importance = TRUE
)
rf_preds <- predict(rf_model, newdata = test_data)
conf_matrix_rf <- confusionMatrix(rf_preds, test_data$heart_attack_risk)
accuracy_rf <- conf_matrix_rf$overall['Accuracy']
precision_rf <- conf_matrix_rf$byClass['Pos Pred Value']
recall_rf <- conf_matrix_rf$byClass['Sensitivity']
f1_rf <- conf_matrix_rf$byClass['F1']
print(conf_matrix_rf)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 568 293
## yes 305 586
##
## Accuracy : 0.6587
## 95% CI : (0.6359, 0.6809)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.3173
##
## Mcnemar's Test P-Value : 0.6528
##
## Sensitivity : 0.6506
## Specificity : 0.6667
## Pos Pred Value : 0.6597
## Neg Pred Value : 0.6577
## Prevalence : 0.4983
## Detection Rate : 0.3242
## Detection Prevalence : 0.4914
## Balanced Accuracy : 0.6586
##
## 'Positive' Class : no
##
metrics_rfs <- rbind(metrics_rfs, data.frame(
Model = "RF Base Model",
Accuracy = as.numeric(accuracy_rf),
Precision = as.numeric(precision_rf),
Recall = as.numeric(recall_rf),
F1 = as.numeric(f1_rf)
))
Random Forest 2
# Random Forest mtry=2
set.seed(105)
# mtry = 2
rf_mtry2 <- randomForest(
heart_attack_risk ~ .,
data = train_data,
ntree = 500,
mtry = 2,
importance = TRUE
)
preds2 <- predict(rf_mtry2, newdata = test_data)
cat("\n--- Confusion Matrix for mtry = 2 ---\n")
##
## --- Confusion Matrix for mtry = 2 ---
conf_matrix_rf_mtry2 <- confusionMatrix(preds2, test_data$heart_attack_risk)
print(conf_matrix_rf_mtry2)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 589 254
## yes 284 625
##
## Accuracy : 0.6929
## 95% CI : (0.6707, 0.7145)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.3858
##
## Mcnemar's Test P-Value : 0.2112
##
## Sensitivity : 0.6747
## Specificity : 0.7110
## Pos Pred Value : 0.6987
## Neg Pred Value : 0.6876
## Prevalence : 0.4983
## Detection Rate : 0.3362
## Detection Prevalence : 0.4812
## Balanced Accuracy : 0.6929
##
## 'Positive' Class : no
##
accuracy_rf_mtry2 <- conf_matrix_rf_mtry2$overall['Accuracy']
precision_rf_mtry2 <- conf_matrix_rf_mtry2$byClass['Pos Pred Value']
recall_rf_mtry2 <- conf_matrix_rf_mtry2$byClass['Sensitivity']
f1_rf_mtry2 <- conf_matrix_rf_mtry2$byClass['F1']
metrics_rfs <- rbind(metrics_rfs, data.frame(
Model = "RF mtry=2",
Accuracy = as.numeric(accuracy_rf_mtry2),
Precision = as.numeric(precision_rf_mtry2),
Recall = as.numeric(recall_rf_mtry2),
F1 = as.numeric(f1_rf_mtry2)
))
Random Forest 3
# Random Forest mtry = 4
rf_mtry4 <- randomForest(
heart_attack_risk ~ .,
data = train_data,
ntree = 500,
mtry = 4,
importance = TRUE
)
preds4 <- predict(rf_mtry4, newdata = test_data)
cat("\n--- Confusion Matrix for mtry = 4 ---\n")
##
## --- Confusion Matrix for mtry = 4 ---
conf_matrix_rf_mtry4 <- confusionMatrix(preds4, test_data$heart_attack_risk)
print(conf_matrix_rf_mtry4)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 557 300
## yes 316 579
##
## Accuracy : 0.6484
## 95% CI : (0.6255, 0.6708)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.2968
##
## Mcnemar's Test P-Value : 0.5456
##
## Sensitivity : 0.6380
## Specificity : 0.6587
## Pos Pred Value : 0.6499
## Neg Pred Value : 0.6469
## Prevalence : 0.4983
## Detection Rate : 0.3179
## Detection Prevalence : 0.4892
## Balanced Accuracy : 0.6484
##
## 'Positive' Class : no
##
accuracy_rf_mtry4 <- conf_matrix_rf_mtry4$overall['Accuracy']
precision_rf_mtry4 <- conf_matrix_rf_mtry4$byClass['Pos Pred Value']
recall_rf_mtry4 <- conf_matrix_rf_mtry4$byClass['Sensitivity']
f1_rf_mtry4 <- conf_matrix_rf_mtry4$byClass['F1']
metrics_rfs <- rbind(metrics_rfs, data.frame(
Model = "RF mtry=4",
Accuracy = as.numeric(accuracy_rf_mtry4),
Precision = as.numeric(precision_rf_mtry4),
Recall = as.numeric(recall_rf_mtry4),
F1 = as.numeric(f1_rf_mtry4)
))
Random Forest 4
# Random forest ntree = 1000
set.seed(105)
# Random forest ntree = 1000
rf_mtry2_ntree1000 <- randomForest(
heart_attack_risk ~ .,
data = train_data,
ntree = 1000,
mtry = 2,
importance = TRUE
)
preds_1000 <- predict(rf_mtry2_ntree1000, newdata = test_data)
cat("\n--- Confusion Matrix for mtry = 2, ntree = 1000 ---\n")
##
## --- Confusion Matrix for mtry = 2, ntree = 1000 ---
conf_matrix_rf_mtry2_ntree1000 <- confusionMatrix(preds_1000, test_data$heart_attack_risk, positive = "no")
print(conf_matrix_rf_mtry2_ntree1000 )
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 601 252
## yes 272 627
##
## Accuracy : 0.7009
## 95% CI : (0.6789, 0.7223)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.4018
##
## Mcnemar's Test P-Value : 0.4065
##
## Sensitivity : 0.6884
## Specificity : 0.7133
## Pos Pred Value : 0.7046
## Neg Pred Value : 0.6974
## Prevalence : 0.4983
## Detection Rate : 0.3430
## Detection Prevalence : 0.4869
## Balanced Accuracy : 0.7009
##
## 'Positive' Class : no
##
accuracy_rf_mtry2_ntree1000 <- conf_matrix_rf_mtry2_ntree1000$overall['Accuracy']
precision_rf_mtry2_ntree1000 <- conf_matrix_rf_mtry2_ntree1000$byClass['Pos Pred Value']
recall_rf_mtry2_ntree1000 <- conf_matrix_rf_mtry2_ntree1000$byClass['Sensitivity']
f1_rf_mtry2_ntree1000 <- conf_matrix_rf_mtry2_ntree1000$byClass['F1']
metrics_rfs<- rbind(metrics_rfs, data.frame(
Model = "Random Forest mtry=2 & ntree=1000",
Accuracy = as.numeric(accuracy_rf_mtry2_ntree1000),
Precision = as.numeric(precision_rf_mtry2_ntree1000),
Recall = as.numeric(recall_rf_mtry2_ntree1000),
F1 = as.numeric(f1_rf_mtry2_ntree1000)
))
# Metrics Table
kable(metrics_rfs, caption = "Random Forest Model Performance Metrics", digits = 3)
Random Forest Model Performance Metrics
| RF Base Model |
0.659 |
0.660 |
0.651 |
0.655 |
| RF mtry=2 |
0.693 |
0.699 |
0.675 |
0.686 |
| RF mtry=4 |
0.648 |
0.650 |
0.638 |
0.644 |
| Random Forest mtry=2 & ntree=1000 |
0.701 |
0.705 |
0.688 |
0.696 |
# Compute ROC curves we must use probabilities for class "no"
rf_probs <- predict(rf_model, newdata = test_data, type = "prob")[, "no"]
roc_rf <- roc(test_data$heart_attack_risk, rf_probs)
## Setting levels: control = no, case = yes
## Setting direction: controls > cases
rf2_probs <- predict(rf_mtry2, newdata = test_data, type = "prob")[, "no"]
roc_rf2 <- roc(test_data$heart_attack_risk, rf2_probs)
## Setting levels: control = no, case = yes
## Setting direction: controls > cases
rf4_probs <- predict(rf_mtry4, newdata = test_data, type = "prob")[, "no"]
roc_rf4 <- roc(test_data$heart_attack_risk, rf4_probs)
## Setting levels: control = no, case = yes
## Setting direction: controls > cases
rf1000_probs <- predict(rf_mtry2_ntree1000, newdata = test_data, type = "prob")[, "no"]
roc_rf1000 <- roc(test_data$heart_attack_risk, rf1000_probs)
## Setting levels: control = no, case = yes
## Setting direction: controls > cases
roc_df <- rbind(
data.frame(Model = "Base Model",
TPR = rev(roc_rf$sensitivities),
FPR = rev(1 - roc_rf$specificities)),
data.frame(Model = "mtry=2",
TPR = rev(roc_rf2$sensitivities),
FPR = rev(1 - roc_rf2$specificities)),
data.frame(Model = "mtry=4",
TPR = rev(roc_rf4$sensitivities),
FPR = rev(1 - roc_rf4$specificities)),
data.frame(Model = "mtry=2, ntree=1000",
TPR = rev(roc_rf1000$sensitivities),
FPR = rev(1 - roc_rf1000$specificities))
)
ggplot(roc_df, aes(x = FPR, y = TPR, color = Model)) +
geom_line(size = 1) +
geom_abline(linetype = "dashed", color = "gray") +
labs(title = "ROC Curves for Random Forest Models",
x = "False Positive Rate",
y = "True Positive Rate") +
theme_minimal() +
theme(legend.title = element_blank())
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Neural Networks
metrics_nns <- data.frame(
Model = character(),
Accuracy = numeric(),
Precision = numeric(),
Recall = numeric(),
F1 = numeric(),
stringsAsFactors = FALSE
)
NN:Model 1
# Title: Baseline Model - No Preprocessing
# Features are raw, not scaled or balanced, simple model architecture
set.seed(105)
train_y <- ifelse(train_data$heart_attack_risk == "yes", 1, 0)
test_y <- ifelse(test_data$heart_attack_risk == "yes", 1, 0)
# Features (no preprocessing!)
train_x <- train_data %>%
select(-heart_attack_risk) %>%
mutate(across(everything(), as.numeric)) %>%
as.matrix()
test_x <- test_data %>%
select(-heart_attack_risk) %>%
mutate(across(everything(), as.numeric)) %>%
as.matrix()
# Focal loss function
focal_loss <- function(gamma = 1.5, alpha = 0.75) { # Adjusted gamma, alpha
function(y_true, y_pred) {
epsilon <- k_epsilon()
y_pred <- k_clip(y_pred, epsilon, 1 - epsilon)
pt_1 <- tf$where(k_equal(y_true, 1), y_pred, k_ones_like(y_pred))
pt_0 <- tf$where(k_equal(y_true, 0), y_pred, k_zeros_like(y_pred))
-alpha * k_pow(1 - pt_1, gamma) * k_log(pt_1) -
(1 - alpha) * k_pow(pt_0, gamma) * k_log(1 - pt_0)
}
}
# Model architecture
model1 <- keras_model_sequential() %>%
layer_dense(units = 64, activation = "relu", input_shape = ncol(train_x)) %>%
layer_dropout(0.3) %>%
layer_dense(units = 32, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
## Traceback (most recent call last):
## File "C:\Users\nicol\OneDrive\53DB~1\VIRTUA~1\tf_now\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 24, in <module>
## import ssl
## File "C:\Users\nicol\AppData\Local\R\win-library\4.3\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
## return _run_hook(name, _hook)
## File "C:\Users\nicol\AppData\Local\R\win-library\4.3\reticulate\python\rpytools\loader.py", line 96, in _run_hook
## module = hook()
## File "C:\Users\nicol\AppData\Local\R\win-library\4.3\reticulate\python\rpytools\loader.py", line 120, in _hook
## return _find_and_load(name, import_)
## File "C:\Users\nicol\ANACON~1\lib\ssl.py", line 99, in <module>
## import _ssl # if we can't import it, let the error propagate
## File "C:\Users\nicol\AppData\Local\R\win-library\4.3\reticulate\python\rpytools\loader.py", line 122, in _find_and_load_hook
## return _run_hook(name, _hook)
## File "C:\Users\nicol\AppData\Local\R\win-library\4.3\reticulate\python\rpytools\loader.py", line 96, in _run_hook
## module = hook()
## File "C:\Users\nicol\AppData\Local\R\win-library\4.3\reticulate\python\rpytools\loader.py", line 120, in _hook
## return _find_and_load(name, import_)
## ImportError: DLL load failed while importing _ssl: The specified module could not be found.
##
##
## Warning: Failed to load ssl module. Continuing without ssl support.
# Compile
model1 %>% compile(
loss = focal_loss(gamma = 1.5, alpha = 0.75), # Adjusted loss function parameters
optimizer = optimizer_adam(learning_rate = 0.0002),
metrics = list("accuracy", metric_auc(name = "auc"))
)
# Train the model
history <- model1 %>% fit(
train_x, train_y,
epochs = 100,
batch_size = 32,
validation_split = 0.2,
verbose = 1
)
## Epoch 1/100
##
## 1/176 [..............................] - ETA: 3:53 - loss: 2.2886 - accuracy: 0.3438 - auc: 0.3417
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## 174/176 [============================>.] - ETA: 0s - loss: 1.7300 - accuracy: 0.4485 - auc: 0.5013
## 176/176 [==============================] - 2s 5ms/step - loss: 1.7293 - accuracy: 0.4492 - auc: 0.5015 - val_loss: 0.1010 - val_accuracy: 0.9002 - val_auc: 0.0000e+00
## Epoch 2/100
##
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## 155/176 [=========================>....] - ETA: 0s - loss: 1.2307 - accuracy: 0.4637 - auc: 0.5030
## 176/176 [==============================] - 1s 3ms/step - loss: 1.2040 - accuracy: 0.4590 - auc: 0.4999 - val_loss: 0.0419 - val_accuracy: 0.9572 - val_auc: 0.0000e+00
## Epoch 3/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.8580 - accuracy: 0.4688 - auc: 0.5024
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## 158/176 [=========================>....] - ETA: 0s - loss: 0.8193 - accuracy: 0.4693 - auc: 0.5036
## 176/176 [==============================] - 1s 3ms/step - loss: 0.8123 - accuracy: 0.4683 - auc: 0.5031 - val_loss: 0.0150 - val_accuracy: 0.9936 - val_auc: 0.0000e+00
## Epoch 4/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.9971 - accuracy: 0.3438 - auc: 0.2857
## 22/176 [==>...........................] - ETA: 0s - loss: 0.6811 - accuracy: 0.4560 - auc: 0.4938
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.5853 - accuracy: 0.4724 - auc: 0.5080 - val_loss: 0.0345 - val_accuracy: 0.9900 - val_auc: 0.0000e+00
## Epoch 5/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.4791 - accuracy: 0.5000 - auc: 0.5169
## 20/176 [==>...........................] - ETA: 0s - loss: 0.5099 - accuracy: 0.4391 - auc: 0.4661
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## 89/176 [==============>...............] - ETA: 0s - loss: 0.4658 - accuracy: 0.4614 - auc: 0.5010
## 112/176 [==================>...........] - ETA: 0s - loss: 0.4627 - accuracy: 0.4615 - auc: 0.4984
## 135/176 [======================>.......] - ETA: 0s - loss: 0.4532 - accuracy: 0.4618 - auc: 0.4992
## 159/176 [==========================>...] - ETA: 0s - loss: 0.4474 - accuracy: 0.4621 - auc: 0.4985
## 176/176 [==============================] - 1s 3ms/step - loss: 0.4391 - accuracy: 0.4647 - auc: 0.5013 - val_loss: 0.0519 - val_accuracy: 0.9907 - val_auc: 0.0000e+00
## Epoch 6/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.3030 - accuracy: 0.4375 - auc: 0.4542
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.3361 - accuracy: 0.4647 - auc: 0.5082
## 176/176 [==============================] - 1s 3ms/step - loss: 0.3350 - accuracy: 0.4622 - auc: 0.5051 - val_loss: 0.0741 - val_accuracy: 0.9900 - val_auc: 0.0000e+00
## Epoch 7/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.4084 - accuracy: 0.3750 - auc: 0.2895
## 24/176 [===>..........................] - ETA: 0s - loss: 0.2866 - accuracy: 0.4714 - auc: 0.5105
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## 88/176 [==============>...............] - ETA: 0s - loss: 0.3033 - accuracy: 0.4716 - auc: 0.5070
## 112/176 [==================>...........] - ETA: 0s - loss: 0.3026 - accuracy: 0.4699 - auc: 0.5015
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## 159/176 [==========================>...] - ETA: 0s - loss: 0.2926 - accuracy: 0.4733 - auc: 0.5069
## 176/176 [==============================] - 1s 3ms/step - loss: 0.2925 - accuracy: 0.4692 - auc: 0.5047 - val_loss: 0.0957 - val_accuracy: 0.9786 - val_auc: 0.0000e+00
## Epoch 8/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.2779 - accuracy: 0.5000 - auc: 0.5568
## 25/176 [===>..........................] - ETA: 0s - loss: 0.2848 - accuracy: 0.4675 - auc: 0.4883
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## 72/176 [===========>..................] - ETA: 0s - loss: 0.2689 - accuracy: 0.4688 - auc: 0.4996
## 95/176 [===============>..............] - ETA: 0s - loss: 0.2663 - accuracy: 0.4734 - auc: 0.5038
## 118/176 [===================>..........] - ETA: 0s - loss: 0.2678 - accuracy: 0.4680 - auc: 0.5001
## 141/176 [=======================>......] - ETA: 0s - loss: 0.2621 - accuracy: 0.4663 - auc: 0.5023
## 165/176 [===========================>..] - ETA: 0s - loss: 0.2574 - accuracy: 0.4678 - auc: 0.5041
## 176/176 [==============================] - 1s 3ms/step - loss: 0.2557 - accuracy: 0.4674 - auc: 0.5045 - val_loss: 0.1136 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 9/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.3315 - accuracy: 0.4688 - auc: 0.5386
## 23/176 [==>...........................] - ETA: 0s - loss: 0.2265 - accuracy: 0.4688 - auc: 0.5426
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## 89/176 [==============>...............] - ETA: 0s - loss: 0.2325 - accuracy: 0.4614 - auc: 0.5142
## 112/176 [==================>...........] - ETA: 0s - loss: 0.2333 - accuracy: 0.4528 - auc: 0.5098
## 136/176 [======================>.......] - ETA: 0s - loss: 0.2284 - accuracy: 0.4580 - auc: 0.5134
## 159/176 [==========================>...] - ETA: 0s - loss: 0.2288 - accuracy: 0.4577 - auc: 0.5140
## 176/176 [==============================] - 1s 3ms/step - loss: 0.2312 - accuracy: 0.4558 - auc: 0.5106 - val_loss: 0.1173 - val_accuracy: 0.9857 - val_auc: 0.0000e+00
## Epoch 10/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.2061 - accuracy: 0.6250 - auc: 0.5099
## 24/176 [===>..........................] - ETA: 0s - loss: 0.2399 - accuracy: 0.4466 - auc: 0.4858
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## 93/176 [==============>...............] - ETA: 0s - loss: 0.2173 - accuracy: 0.4587 - auc: 0.5226
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.2161 - accuracy: 0.4513 - auc: 0.5099
## 176/176 [==============================] - 1s 3ms/step - loss: 0.2157 - accuracy: 0.4522 - auc: 0.5088 - val_loss: 0.1093 - val_accuracy: 0.9900 - val_auc: 0.0000e+00
## Epoch 11/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1759 - accuracy: 0.4688 - auc: 0.6113
## 23/176 [==>...........................] - ETA: 0s - loss: 0.2248 - accuracy: 0.4389 - auc: 0.4869
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## 93/176 [==============>...............] - ETA: 0s - loss: 0.2140 - accuracy: 0.4378 - auc: 0.4877
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## 165/176 [===========================>..] - ETA: 0s - loss: 0.2088 - accuracy: 0.4362 - auc: 0.4892
## 176/176 [==============================] - 0s 3ms/step - loss: 0.2079 - accuracy: 0.4369 - auc: 0.4909 - val_loss: 0.2466 - val_accuracy: 0.6657 - val_auc: 0.0000e+00
## Epoch 12/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1695 - accuracy: 0.5312 - auc: 0.5290
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1951 - accuracy: 0.4466 - auc: 0.5026
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1877 - accuracy: 0.4306 - auc: 0.5081 - val_loss: 0.1770 - val_accuracy: 0.9729 - val_auc: 0.0000e+00
## Epoch 13/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1950 - accuracy: 0.3750 - auc: 0.4848
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1814 - accuracy: 0.4246 - auc: 0.4927 - val_loss: 0.1781 - val_accuracy: 0.9793 - val_auc: 0.0000e+00
## Epoch 14/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1790 - accuracy: 0.4375 - auc: 0.5152
## 22/176 [==>...........................] - ETA: 0s - loss: 0.1806 - accuracy: 0.3963 - auc: 0.4883
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## 164/176 [==========================>...] - ETA: 0s - loss: 0.1797 - accuracy: 0.4230 - auc: 0.5001
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1794 - accuracy: 0.4244 - auc: 0.4984 - val_loss: 0.0966 - val_accuracy: 0.9993 - val_auc: 0.0000e+00
## Epoch 15/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1729 - accuracy: 0.4062 - auc: 0.5042
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1630 - accuracy: 0.4457 - auc: 0.5267
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## 119/176 [===================>..........] - ETA: 0s - loss: 0.1645 - accuracy: 0.4275 - auc: 0.5139
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1671 - accuracy: 0.4226 - auc: 0.5076 - val_loss: 0.1608 - val_accuracy: 0.9850 - val_auc: 0.0000e+00
## Epoch 16/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1479 - accuracy: 0.4062 - auc: 0.5023
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1689 - accuracy: 0.4187 - auc: 0.4952 - val_loss: 0.1958 - val_accuracy: 0.9729 - val_auc: 0.0000e+00
## Epoch 17/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1481 - accuracy: 0.3125 - auc: 0.5571
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1651 - accuracy: 0.4246 - auc: 0.5037 - val_loss: 0.1625 - val_accuracy: 0.9872 - val_auc: 0.0000e+00
## Epoch 18/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1567 - accuracy: 0.4062 - auc: 0.3874
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1580 - accuracy: 0.4158 - auc: 0.5225
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1580 - accuracy: 0.4194 - auc: 0.5203 - val_loss: 0.2221 - val_accuracy: 0.9608 - val_auc: 0.0000e+00
## Epoch 19/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1440 - accuracy: 0.4375 - auc: 0.6234
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## 165/176 [===========================>..] - ETA: 0s - loss: 0.1555 - accuracy: 0.4186 - auc: 0.5189
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1556 - accuracy: 0.4206 - auc: 0.5197 - val_loss: 0.1333 - val_accuracy: 0.9943 - val_auc: 0.0000e+00
## Epoch 20/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1547 - accuracy: 0.4375 - auc: 0.6523
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## 133/176 [=====================>........] - ETA: 0s - loss: 0.1568 - accuracy: 0.4131 - auc: 0.5130
## 156/176 [=========================>....] - ETA: 0s - loss: 0.1572 - accuracy: 0.4143 - auc: 0.5108
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1566 - accuracy: 0.4133 - auc: 0.5143 - val_loss: 0.2613 - val_accuracy: 0.4925 - val_auc: 0.0000e+00
## Epoch 21/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1542 - accuracy: 0.5938 - auc: 0.6147
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1598 - accuracy: 0.4497 - auc: 0.5074
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## 114/176 [==================>...........] - ETA: 0s - loss: 0.1572 - accuracy: 0.4120 - auc: 0.5030
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## 159/176 [==========================>...] - ETA: 0s - loss: 0.1560 - accuracy: 0.4090 - auc: 0.5060
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1558 - accuracy: 0.4091 - auc: 0.5069 - val_loss: 0.2212 - val_accuracy: 0.9458 - val_auc: 0.0000e+00
## Epoch 22/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1951 - accuracy: 0.4688 - auc: 0.4881
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1532 - accuracy: 0.4039 - auc: 0.5158
## 114/176 [==================>...........] - ETA: 0s - loss: 0.1540 - accuracy: 0.4002 - auc: 0.5112
## 138/176 [======================>.......] - ETA: 0s - loss: 0.1535 - accuracy: 0.3999 - auc: 0.5110
## 162/176 [==========================>...] - ETA: 0s - loss: 0.1536 - accuracy: 0.3985 - auc: 0.5127
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1537 - accuracy: 0.4034 - auc: 0.5121 - val_loss: 0.1119 - val_accuracy: 0.9964 - val_auc: 0.0000e+00
## Epoch 23/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1531 - accuracy: 0.4688 - auc: 0.4484
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## 93/176 [==============>...............] - ETA: 0s - loss: 0.1538 - accuracy: 0.4076 - auc: 0.5079
## 118/176 [===================>..........] - ETA: 0s - loss: 0.1530 - accuracy: 0.4044 - auc: 0.5109
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## 164/176 [==========================>...] - ETA: 0s - loss: 0.1516 - accuracy: 0.4055 - auc: 0.5150
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1514 - accuracy: 0.4057 - auc: 0.5158 - val_loss: 0.1911 - val_accuracy: 0.9822 - val_auc: 0.0000e+00
## Epoch 24/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1352 - accuracy: 0.3750 - auc: 0.6114
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1479 - accuracy: 0.4313 - auc: 0.5269
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## 118/176 [===================>..........] - ETA: 0s - loss: 0.1496 - accuracy: 0.4044 - auc: 0.5172
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## 164/176 [==========================>...] - ETA: 0s - loss: 0.1502 - accuracy: 0.4017 - auc: 0.5178
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1503 - accuracy: 0.4057 - auc: 0.5201 - val_loss: 0.1519 - val_accuracy: 0.9907 - val_auc: 0.0000e+00
## Epoch 25/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1386 - accuracy: 0.2812 - auc: 0.6198
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1510 - accuracy: 0.4232 - auc: 0.5235
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1517 - accuracy: 0.4050 - auc: 0.5116 - val_loss: 0.1960 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 26/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1467 - accuracy: 0.2812 - auc: 0.4565
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1504 - accuracy: 0.4016 - auc: 0.5156 - val_loss: 0.1431 - val_accuracy: 0.9943 - val_auc: 0.0000e+00
## Epoch 27/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1580 - accuracy: 0.4062 - auc: 0.4514
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.1483 - accuracy: 0.3931 - auc: 0.5162
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1490 - accuracy: 0.3969 - auc: 0.5137 - val_loss: 0.1377 - val_accuracy: 0.9929 - val_auc: 0.0000e+00
## Epoch 28/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1567 - accuracy: 0.2812 - auc: 0.4807
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1487 - accuracy: 0.3953 - auc: 0.5092 - val_loss: 0.1806 - val_accuracy: 0.9914 - val_auc: 0.0000e+00
## Epoch 29/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1359 - accuracy: 0.4688 - auc: 0.6187
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1439 - accuracy: 0.4338 - auc: 0.5473
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1477 - accuracy: 0.4114 - auc: 0.5267 - val_loss: 0.1948 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 30/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1415 - accuracy: 0.3750 - auc: 0.4205
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1504 - accuracy: 0.4150 - auc: 0.5242
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## 164/176 [==========================>...] - ETA: 0s - loss: 0.1481 - accuracy: 0.3998 - auc: 0.5153
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1481 - accuracy: 0.4001 - auc: 0.5126 - val_loss: 0.1487 - val_accuracy: 0.9957 - val_auc: 0.0000e+00
## Epoch 31/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1480 - accuracy: 0.3750 - auc: 0.3896
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1489 - accuracy: 0.3825 - auc: 0.5159
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## 95/176 [===============>..............] - ETA: 0s - loss: 0.1494 - accuracy: 0.3984 - auc: 0.5103
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1488 - accuracy: 0.3975 - auc: 0.5053 - val_loss: 0.1823 - val_accuracy: 0.9800 - val_auc: 0.0000e+00
## Epoch 32/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1602 - accuracy: 0.2812 - auc: 0.4919
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1447 - accuracy: 0.3940 - auc: 0.5217
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## 163/176 [==========================>...] - ETA: 0s - loss: 0.1465 - accuracy: 0.3999 - auc: 0.5185
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1468 - accuracy: 0.3982 - auc: 0.5156 - val_loss: 0.1450 - val_accuracy: 0.9943 - val_auc: 0.0000e+00
## Epoch 33/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1507 - accuracy: 0.4062 - auc: 0.4762
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1459 - accuracy: 0.3818 - auc: 0.4973
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1472 - accuracy: 0.3880 - auc: 0.5077 - val_loss: 0.1155 - val_accuracy: 0.9964 - val_auc: 0.0000e+00
## Epoch 34/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1425 - accuracy: 0.5000 - auc: 0.5586
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1477 - accuracy: 0.3989 - auc: 0.5094 - val_loss: 0.1841 - val_accuracy: 0.9872 - val_auc: 0.0000e+00
## Epoch 35/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1336 - accuracy: 0.4062 - auc: 0.5500
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1438 - accuracy: 0.3802 - auc: 0.5309
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1466 - accuracy: 0.3927 - auc: 0.5057 - val_loss: 0.2025 - val_accuracy: 0.9843 - val_auc: 0.0000e+00
## Epoch 36/100
##
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## 24/176 [===>..........................] - ETA: 0s - loss: 0.1458 - accuracy: 0.3984 - auc: 0.5258
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1468 - accuracy: 0.3946 - auc: 0.5184 - val_loss: 0.1788 - val_accuracy: 0.9907 - val_auc: 0.0000e+00
## Epoch 37/100
##
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1473 - accuracy: 0.3889 - auc: 0.5056 - val_loss: 0.2194 - val_accuracy: 0.9708 - val_auc: 0.0000e+00
## Epoch 38/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1463 - accuracy: 0.4688 - auc: 0.5729
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## 175/176 [============================>.] - ETA: 0s - loss: 0.1461 - accuracy: 0.3871 - auc: 0.5154
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1460 - accuracy: 0.3869 - auc: 0.5155 - val_loss: 0.1983 - val_accuracy: 0.9779 - val_auc: 0.0000e+00
## Epoch 39/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1540 - accuracy: 0.3438 - auc: 0.5693
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1462 - accuracy: 0.3944 - auc: 0.5221 - val_loss: 0.1669 - val_accuracy: 0.9907 - val_auc: 0.0000e+00
## Epoch 40/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1777 - accuracy: 0.5000 - auc: 0.5510
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## 154/176 [=========================>....] - ETA: 0s - loss: 0.1471 - accuracy: 0.3872 - auc: 0.5040
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1469 - accuracy: 0.3910 - auc: 0.5089 - val_loss: 0.2251 - val_accuracy: 0.9672 - val_auc: 0.0000e+00
## Epoch 41/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1522 - accuracy: 0.4688 - auc: 0.6569
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## 93/176 [==============>...............] - ETA: 0s - loss: 0.1466 - accuracy: 0.3921 - auc: 0.5124
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## 162/176 [==========================>...] - ETA: 0s - loss: 0.1453 - accuracy: 0.3931 - auc: 0.5253
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1453 - accuracy: 0.3900 - auc: 0.5235 - val_loss: 0.2091 - val_accuracy: 0.9708 - val_auc: 0.0000e+00
## Epoch 42/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1202 - accuracy: 0.4062 - auc: 0.8636
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## 94/176 [===============>..............] - ETA: 0s - loss: 0.1449 - accuracy: 0.4023 - auc: 0.5372
## 117/176 [==================>...........] - ETA: 0s - loss: 0.1451 - accuracy: 0.3993 - auc: 0.5341
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## 163/176 [==========================>...] - ETA: 0s - loss: 0.1452 - accuracy: 0.4047 - auc: 0.5353
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1453 - accuracy: 0.4025 - auc: 0.5328 - val_loss: 0.2108 - val_accuracy: 0.9793 - val_auc: 0.0000e+00
## Epoch 43/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1418 - accuracy: 0.3125 - auc: 0.3575
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## 94/176 [===============>..............] - ETA: 0s - loss: 0.1465 - accuracy: 0.3920 - auc: 0.5014
## 118/176 [===================>..........] - ETA: 0s - loss: 0.1460 - accuracy: 0.3877 - auc: 0.5096
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## 176/176 [==============================] - 0s 3ms/step - loss: 0.1454 - accuracy: 0.3880 - auc: 0.5207 - val_loss: 0.2294 - val_accuracy: 0.9522 - val_auc: 0.0000e+00
## Epoch 44/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1384 - accuracy: 0.4062 - auc: 0.6583
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## 164/176 [==========================>...] - ETA: 0s - loss: 0.1442 - accuracy: 0.3952 - auc: 0.5322
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1442 - accuracy: 0.3957 - auc: 0.5340 - val_loss: 0.1707 - val_accuracy: 0.9843 - val_auc: 0.0000e+00
## Epoch 45/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1340 - accuracy: 0.3125 - auc: 0.6184
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1440 - accuracy: 0.3966 - auc: 0.5272 - val_loss: 0.1698 - val_accuracy: 0.9914 - val_auc: 0.0000e+00
## Epoch 46/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1479 - accuracy: 0.5000 - auc: 0.6255
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1437 - accuracy: 0.3880 - auc: 0.5317 - val_loss: 0.2045 - val_accuracy: 0.9822 - val_auc: 0.0000e+00
## Epoch 47/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1461 - accuracy: 0.4375 - auc: 0.5729
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## 163/176 [==========================>...] - ETA: 0s - loss: 0.1459 - accuracy: 0.3917 - auc: 0.5239
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1459 - accuracy: 0.3891 - auc: 0.5225 - val_loss: 0.1922 - val_accuracy: 0.9779 - val_auc: 0.0000e+00
## Epoch 48/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1361 - accuracy: 0.3438 - auc: 0.6386
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1443 - accuracy: 0.3878 - auc: 0.5219 - val_loss: 0.1929 - val_accuracy: 0.9857 - val_auc: 0.0000e+00
## Epoch 49/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1543 - accuracy: 0.5625 - auc: 0.4940
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1438 - accuracy: 0.3868 - auc: 0.5208 - val_loss: 0.1766 - val_accuracy: 0.9872 - val_auc: 0.0000e+00
## Epoch 50/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1329 - accuracy: 0.2188 - auc: 0.5449
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1431 - accuracy: 0.4049 - auc: 0.5246
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1435 - accuracy: 0.3879 - auc: 0.5383
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1440 - accuracy: 0.3909 - auc: 0.5346 - val_loss: 0.1887 - val_accuracy: 0.9850 - val_auc: 0.0000e+00
## Epoch 51/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1364 - accuracy: 0.3750 - auc: 0.6182
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## 162/176 [==========================>...] - ETA: 0s - loss: 0.1438 - accuracy: 0.3883 - auc: 0.5317
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1439 - accuracy: 0.3889 - auc: 0.5307 - val_loss: 0.1893 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 52/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1522 - accuracy: 0.4688 - auc: 0.5000
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## 163/176 [==========================>...] - ETA: 0s - loss: 0.1448 - accuracy: 0.3921 - auc: 0.5160
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1448 - accuracy: 0.3916 - auc: 0.5167 - val_loss: 0.1499 - val_accuracy: 0.9922 - val_auc: 0.0000e+00
## Epoch 53/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1389 - accuracy: 0.4688 - auc: 0.5385
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1436 - accuracy: 0.3879 - auc: 0.5158
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## 162/176 [==========================>...] - ETA: 0s - loss: 0.1442 - accuracy: 0.3895 - auc: 0.5115
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1442 - accuracy: 0.3916 - auc: 0.5100 - val_loss: 0.1420 - val_accuracy: 0.9950 - val_auc: 0.0000e+00
## Epoch 54/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1413 - accuracy: 0.5938 - auc: 0.4939
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1443 - accuracy: 0.3894 - auc: 0.5190 - val_loss: 0.1483 - val_accuracy: 0.9943 - val_auc: 0.0000e+00
## Epoch 55/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1461 - accuracy: 0.4062 - auc: 0.4187
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## 176/176 [==============================] - 0s 3ms/step - loss: 0.1436 - accuracy: 0.3898 - auc: 0.5178 - val_loss: 0.1593 - val_accuracy: 0.9886 - val_auc: 0.0000e+00
## Epoch 56/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1472 - accuracy: 0.4375 - auc: 0.4921
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1435 - accuracy: 0.3893 - auc: 0.5292 - val_loss: 0.1855 - val_accuracy: 0.9800 - val_auc: 0.0000e+00
## Epoch 57/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1356 - accuracy: 0.3750 - auc: 0.6299
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1433 - accuracy: 0.3898 - auc: 0.5309 - val_loss: 0.1404 - val_accuracy: 0.9943 - val_auc: 0.0000e+00
## Epoch 58/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1387 - accuracy: 0.4062 - auc: 0.7024
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1441 - accuracy: 0.3918 - auc: 0.5245 - val_loss: 0.1491 - val_accuracy: 0.9936 - val_auc: 0.0000e+00
## Epoch 59/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1435 - accuracy: 0.3125 - auc: 0.5205
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1438 - accuracy: 0.3914 - auc: 0.5271 - val_loss: 0.1740 - val_accuracy: 0.9850 - val_auc: 0.0000e+00
## Epoch 60/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1467 - accuracy: 0.5312 - auc: 0.6098
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1431 - accuracy: 0.3905 - auc: 0.5206 - val_loss: 0.1904 - val_accuracy: 0.9865 - val_auc: 0.0000e+00
## Epoch 61/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1501 - accuracy: 0.5000 - auc: 0.5820
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## 165/176 [===========================>..] - ETA: 0s - loss: 0.1433 - accuracy: 0.3915 - auc: 0.5282
## 176/176 [==============================] - 0s 3ms/step - loss: 0.1432 - accuracy: 0.3916 - auc: 0.5295 - val_loss: 0.1908 - val_accuracy: 0.9729 - val_auc: 0.0000e+00
## Epoch 62/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1385 - accuracy: 0.3125 - auc: 0.4928
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.1432 - accuracy: 0.3944 - auc: 0.5376
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1433 - accuracy: 0.3943 - auc: 0.5349 - val_loss: 0.1562 - val_accuracy: 0.9914 - val_auc: 0.0000e+00
## Epoch 63/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1439 - accuracy: 0.3125 - auc: 0.6091
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## 165/176 [===========================>..] - ETA: 0s - loss: 0.1426 - accuracy: 0.3886 - auc: 0.5330
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1428 - accuracy: 0.3907 - auc: 0.5315 - val_loss: 0.1675 - val_accuracy: 0.9857 - val_auc: 0.0000e+00
## Epoch 64/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1368 - accuracy: 0.3750 - auc: 0.5114
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1433 - accuracy: 0.3905 - auc: 0.5197 - val_loss: 0.1649 - val_accuracy: 0.9872 - val_auc: 0.0000e+00
## Epoch 65/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1357 - accuracy: 0.3125 - auc: 0.6589
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1428 - accuracy: 0.3946 - auc: 0.5334 - val_loss: 0.1624 - val_accuracy: 0.9886 - val_auc: 0.0000e+00
## Epoch 66/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1433 - accuracy: 0.3438 - auc: 0.4935
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1432 - accuracy: 0.3930 - auc: 0.5346 - val_loss: 0.1487 - val_accuracy: 0.9922 - val_auc: 0.0000e+00
## Epoch 67/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1461 - accuracy: 0.3750 - auc: 0.5688
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1431 - accuracy: 0.3925 - auc: 0.5287 - val_loss: 0.1976 - val_accuracy: 0.9779 - val_auc: 0.0000e+00
## Epoch 68/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1363 - accuracy: 0.3438 - auc: 0.4541
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## 68/176 [==========>...................] - ETA: 0s - loss: 0.1415 - accuracy: 0.3869 - auc: 0.5313
## 91/176 [==============>...............] - ETA: 0s - loss: 0.1424 - accuracy: 0.3977 - auc: 0.5430
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1428 - accuracy: 0.3964 - auc: 0.5337 - val_loss: 0.1666 - val_accuracy: 0.9893 - val_auc: 0.0000e+00
## Epoch 69/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1442 - accuracy: 0.3750 - auc: 0.3918
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1433 - accuracy: 0.3896 - auc: 0.5355 - val_loss: 0.1900 - val_accuracy: 0.9815 - val_auc: 0.0000e+00
## Epoch 70/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1448 - accuracy: 0.3438 - auc: 0.4136
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1434 - accuracy: 0.3927 - auc: 0.5240 - val_loss: 0.1985 - val_accuracy: 0.9765 - val_auc: 0.0000e+00
## Epoch 71/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1318 - accuracy: 0.3438 - auc: 0.6763
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1430 - accuracy: 0.3910 - auc: 0.5243 - val_loss: 0.1731 - val_accuracy: 0.9850 - val_auc: 0.0000e+00
## Epoch 72/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1390 - accuracy: 0.2812 - auc: 0.4922
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1440 - accuracy: 0.3700 - auc: 0.5177
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1435 - accuracy: 0.3889 - auc: 0.5186
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## 156/176 [=========================>....] - ETA: 0s - loss: 0.1437 - accuracy: 0.3882 - auc: 0.5199
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1435 - accuracy: 0.3882 - auc: 0.5226 - val_loss: 0.1755 - val_accuracy: 0.9879 - val_auc: 0.0000e+00
## Epoch 73/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1523 - accuracy: 0.5312 - auc: 0.4235
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1430 - accuracy: 0.3958 - auc: 0.5046
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## 93/176 [==============>...............] - ETA: 0s - loss: 0.1428 - accuracy: 0.3958 - auc: 0.5229
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.1428 - accuracy: 0.3905 - auc: 0.5171
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1429 - accuracy: 0.3928 - auc: 0.5188 - val_loss: 0.1693 - val_accuracy: 0.9857 - val_auc: 0.0000e+00
## Epoch 74/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1351 - accuracy: 0.3438 - auc: 0.5700
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1412 - accuracy: 0.4062 - auc: 0.5627
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## 91/176 [==============>...............] - ETA: 0s - loss: 0.1424 - accuracy: 0.3922 - auc: 0.5468
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## 159/176 [==========================>...] - ETA: 0s - loss: 0.1423 - accuracy: 0.3905 - auc: 0.5389
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1424 - accuracy: 0.3927 - auc: 0.5364 - val_loss: 0.1646 - val_accuracy: 0.9865 - val_auc: 0.0000e+00
## Epoch 75/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1303 - accuracy: 0.2500 - auc: 0.7829
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1422 - accuracy: 0.3828 - auc: 0.5251
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1433 - accuracy: 0.3916 - auc: 0.5173 - val_loss: 0.1682 - val_accuracy: 0.9872 - val_auc: 0.0000e+00
## Epoch 76/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1383 - accuracy: 0.3125 - auc: 0.4783
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1424 - accuracy: 0.3898 - auc: 0.5364 - val_loss: 0.1850 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 77/100
##
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1430 - accuracy: 0.3905 - auc: 0.5337 - val_loss: 0.2125 - val_accuracy: 0.9629 - val_auc: 0.0000e+00
## Epoch 78/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1436 - accuracy: 0.4062 - auc: 0.4688
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## 89/176 [==============>...............] - ETA: 0s - loss: 0.1424 - accuracy: 0.4048 - auc: 0.5391
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1423 - accuracy: 0.3969 - auc: 0.5405 - val_loss: 0.1971 - val_accuracy: 0.9665 - val_auc: 0.0000e+00
## Epoch 79/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1383 - accuracy: 0.3750 - auc: 0.5390
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## 90/176 [==============>...............] - ETA: 0s - loss: 0.1422 - accuracy: 0.3882 - auc: 0.5426
## 115/176 [==================>...........] - ETA: 0s - loss: 0.1425 - accuracy: 0.3891 - auc: 0.5351
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## 172/176 [============================>.] - ETA: 0s - loss: 0.1428 - accuracy: 0.3937 - auc: 0.5335
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1428 - accuracy: 0.3937 - auc: 0.5328 - val_loss: 0.1592 - val_accuracy: 0.9886 - val_auc: 0.0000e+00
## Epoch 80/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1401 - accuracy: 0.4688 - auc: 0.5873
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## 90/176 [==============>...............] - ETA: 0s - loss: 0.1421 - accuracy: 0.3868 - auc: 0.5409
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1423 - accuracy: 0.3905 - auc: 0.5361 - val_loss: 0.1683 - val_accuracy: 0.9865 - val_auc: 0.0000e+00
## Epoch 81/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1415 - accuracy: 0.4062 - auc: 0.5750
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## 165/176 [===========================>..] - ETA: 0s - loss: 0.1424 - accuracy: 0.3919 - auc: 0.5372
## 176/176 [==============================] - 0s 3ms/step - loss: 0.1423 - accuracy: 0.3925 - auc: 0.5385 - val_loss: 0.1781 - val_accuracy: 0.9779 - val_auc: 0.0000e+00
## Epoch 82/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1451 - accuracy: 0.4688 - auc: 0.5258
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## 114/176 [==================>...........] - ETA: 0s - loss: 0.1425 - accuracy: 0.3917 - auc: 0.5333
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.1426 - accuracy: 0.3915 - auc: 0.5362
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1426 - accuracy: 0.3923 - auc: 0.5352 - val_loss: 0.1818 - val_accuracy: 0.9872 - val_auc: 0.0000e+00
## Epoch 83/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1487 - accuracy: 0.4375 - auc: 0.4444
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1423 - accuracy: 0.3997 - auc: 0.5515
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## 90/176 [==============>...............] - ETA: 0s - loss: 0.1423 - accuracy: 0.3917 - auc: 0.5432
## 114/176 [==================>...........] - ETA: 0s - loss: 0.1426 - accuracy: 0.3947 - auc: 0.5399
## 136/176 [======================>.......] - ETA: 0s - loss: 0.1427 - accuracy: 0.3922 - auc: 0.5345
## 159/176 [==========================>...] - ETA: 0s - loss: 0.1429 - accuracy: 0.3970 - auc: 0.5330
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1426 - accuracy: 0.3930 - auc: 0.5316 - val_loss: 0.1991 - val_accuracy: 0.9722 - val_auc: 0.0000e+00
## Epoch 84/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1347 - accuracy: 0.3438 - auc: 0.5682
## 22/176 [==>...........................] - ETA: 0s - loss: 0.1396 - accuracy: 0.3849 - auc: 0.5676
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## 89/176 [==============>...............] - ETA: 0s - loss: 0.1415 - accuracy: 0.3862 - auc: 0.5402
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## 158/176 [=========================>....] - ETA: 0s - loss: 0.1419 - accuracy: 0.3930 - auc: 0.5459
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1421 - accuracy: 0.3927 - auc: 0.5411 - val_loss: 0.1738 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 85/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1404 - accuracy: 0.3438 - auc: 0.5303
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## 168/176 [===========================>..] - ETA: 0s - loss: 0.1426 - accuracy: 0.3903 - auc: 0.5334
## 176/176 [==============================] - 0s 3ms/step - loss: 0.1425 - accuracy: 0.3900 - auc: 0.5341 - val_loss: 0.1858 - val_accuracy: 0.9786 - val_auc: 0.0000e+00
## Epoch 86/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1360 - accuracy: 0.3125 - auc: 0.5411
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## 157/176 [=========================>....] - ETA: 0s - loss: 0.1414 - accuracy: 0.3905 - auc: 0.5454
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1419 - accuracy: 0.3950 - auc: 0.5422 - val_loss: 0.1584 - val_accuracy: 0.9843 - val_auc: 0.0000e+00
## Epoch 87/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1316 - accuracy: 0.5000 - auc: 0.7471
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## 166/176 [===========================>..] - ETA: 0s - loss: 0.1420 - accuracy: 0.3959 - auc: 0.5462
## 176/176 [==============================] - 0s 3ms/step - loss: 0.1420 - accuracy: 0.3953 - auc: 0.5457 - val_loss: 0.1846 - val_accuracy: 0.9793 - val_auc: 0.0000e+00
## Epoch 88/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1396 - accuracy: 0.4375 - auc: 0.7186
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1419 - accuracy: 0.3916 - auc: 0.5434 - val_loss: 0.1787 - val_accuracy: 0.9808 - val_auc: 0.0000e+00
## Epoch 89/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1433 - accuracy: 0.3750 - auc: 0.4625
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1423 - accuracy: 0.3997 - auc: 0.5480
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1417 - accuracy: 0.3944 - auc: 0.5477
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## 163/176 [==========================>...] - ETA: 0s - loss: 0.1420 - accuracy: 0.3878 - auc: 0.5416
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1421 - accuracy: 0.3891 - auc: 0.5407 - val_loss: 0.1728 - val_accuracy: 0.9857 - val_auc: 0.0000e+00
## Epoch 90/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1474 - accuracy: 0.5000 - auc: 0.5000
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## 91/176 [==============>...............] - ETA: 0s - loss: 0.1420 - accuracy: 0.3929 - auc: 0.5424
## 114/176 [==================>...........] - ETA: 0s - loss: 0.1420 - accuracy: 0.3923 - auc: 0.5382
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## 162/176 [==========================>...] - ETA: 0s - loss: 0.1421 - accuracy: 0.3933 - auc: 0.5352
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1419 - accuracy: 0.3925 - auc: 0.5376 - val_loss: 0.1864 - val_accuracy: 0.9779 - val_auc: 0.0000e+00
## Epoch 91/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1428 - accuracy: 0.4062 - auc: 0.6448
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1413 - accuracy: 0.3738 - auc: 0.5405
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## 167/176 [===========================>..] - ETA: 0s - loss: 0.1420 - accuracy: 0.3915 - auc: 0.5395
## 176/176 [==============================] - 0s 3ms/step - loss: 0.1421 - accuracy: 0.3903 - auc: 0.5362 - val_loss: 0.1778 - val_accuracy: 0.9772 - val_auc: 0.0000e+00
## Epoch 92/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1399 - accuracy: 0.5000 - auc: 0.6052
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## 93/176 [==============>...............] - ETA: 0s - loss: 0.1406 - accuracy: 0.3871 - auc: 0.5511
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## 161/176 [==========================>...] - ETA: 0s - loss: 0.1414 - accuracy: 0.3936 - auc: 0.5488
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1417 - accuracy: 0.3957 - auc: 0.5466 - val_loss: 0.1668 - val_accuracy: 0.9793 - val_auc: 0.0000e+00
## Epoch 93/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1388 - accuracy: 0.3125 - auc: 0.5705
## 25/176 [===>..........................] - ETA: 0s - loss: 0.1413 - accuracy: 0.4013 - auc: 0.5323
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## 94/176 [===============>..............] - ETA: 0s - loss: 0.1419 - accuracy: 0.4019 - auc: 0.5434
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## 165/176 [===========================>..] - ETA: 0s - loss: 0.1418 - accuracy: 0.3949 - auc: 0.5458
## 176/176 [==============================] - 0s 3ms/step - loss: 0.1418 - accuracy: 0.3934 - auc: 0.5448 - val_loss: 0.1927 - val_accuracy: 0.9736 - val_auc: 0.0000e+00
## Epoch 94/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1527 - accuracy: 0.4688 - auc: 0.4235
## 24/176 [===>..........................] - ETA: 0s - loss: 0.1397 - accuracy: 0.3984 - auc: 0.5823
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1412 - accuracy: 0.3954 - auc: 0.5552
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## 163/176 [==========================>...] - ETA: 0s - loss: 0.1419 - accuracy: 0.3913 - auc: 0.5415
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1419 - accuracy: 0.3916 - auc: 0.5437 - val_loss: 0.1799 - val_accuracy: 0.9751 - val_auc: 0.0000e+00
## Epoch 95/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1327 - accuracy: 0.3750 - auc: 0.7227
## 23/176 [==>...........................] - ETA: 0s - loss: 0.1430 - accuracy: 0.4158 - auc: 0.5249
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## 89/176 [==============>...............] - ETA: 0s - loss: 0.1420 - accuracy: 0.4080 - auc: 0.5385
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1419 - accuracy: 0.3978 - auc: 0.5384 - val_loss: 0.2062 - val_accuracy: 0.9530 - val_auc: 0.0000e+00
## Epoch 96/100
##
## 1/176 [..............................] - ETA: 0s - loss: 0.1439 - accuracy: 0.5000 - auc: 0.5385
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## 92/176 [==============>...............] - ETA: 0s - loss: 0.1412 - accuracy: 0.4029 - auc: 0.5605
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1416 - accuracy: 0.3964 - auc: 0.5542 - val_loss: 0.1774 - val_accuracy: 0.9729 - val_auc: 0.0000e+00
## Epoch 97/100
##
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## 162/176 [==========================>...] - ETA: 0s - loss: 0.1413 - accuracy: 0.3972 - auc: 0.5552
## 176/176 [==============================] - 1s 3ms/step - loss: 0.1414 - accuracy: 0.3966 - auc: 0.5546 - val_loss: 0.1829 - val_accuracy: 0.9694 - val_auc: 0.0000e+00
## Epoch 98/100
##
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1421 - accuracy: 0.3935 - auc: 0.5467 - val_loss: 0.1939 - val_accuracy: 0.9701 - val_auc: 0.0000e+00
## Epoch 99/100
##
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1417 - accuracy: 0.3943 - auc: 0.5491 - val_loss: 0.1846 - val_accuracy: 0.9722 - val_auc: 0.0000e+00
## Epoch 100/100
##
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## 176/176 [==============================] - 1s 3ms/step - loss: 0.1416 - accuracy: 0.3976 - auc: 0.5536 - val_loss: 0.1711 - val_accuracy: 0.9751 - val_auc: 0.0000e+00
# Predict
pred_probs_nn1 <- model1 %>% predict(test_x)
## 55/55 - 0s - 185ms/epoch - 3ms/step
pred_classes_nn1 <- ifelse(pred_probs_nn1 > 0.5, "yes", "no")
# Evaluate
confusion_matrix_nn1<-confusionMatrix(
factor(pred_classes_nn1, levels = c("no", "yes")),
factor(ifelse(test_y == 1, "yes", "no"), levels = c("no", "yes"))
)
print(confusion_matrix_nn1)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 18 17
## yes 855 862
##
## Accuracy : 0.5023
## 95% CI : (0.4786, 0.526)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : 0.4905
##
## Kappa : 0.0013
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.02062
## Specificity : 0.98066
## Pos Pred Value : 0.51429
## Neg Pred Value : 0.50204
## Prevalence : 0.49829
## Detection Rate : 0.01027
## Detection Prevalence : 0.01998
## Balanced Accuracy : 0.50064
##
## 'Positive' Class : no
##
accuracy_nn <- confusion_matrix_nn1$overall['Accuracy']
precision_nn <- confusion_matrix_nn1$byClass['Pos Pred Value']
recall_nn <- confusion_matrix_nn1$byClass['Sensitivity']
f1_nn <- confusion_matrix_nn1$byClass['F1']
metrics_nns <- rbind(metrics_nns, data.frame(
Model = "NN Base Model",
Accuracy = as.numeric(accuracy_nn),
Precision = as.numeric(precision_nn),
Recall = as.numeric(recall_nn),
F1 = as.numeric(f1_nn)
))
NN:Model 2
# Title: Deep Neural Network (Scaled + SMOTE Balanced)
# Description: With SMOTE oversampling, scaled features, larger architecture
set.seed(105)
# Convert the target to factor for SMOTE
train_data$heart_attack_risk <- factor(train_data$heart_attack_risk, levels = c("no", "yes"))
# Apply SMOTE to balance the training set
train_balanced <- SMOTE(heart_attack_risk ~ ., data = train_data, perc.over = 200, perc.under = 150)
train_y <- ifelse(train_balanced$heart_attack_risk == "yes", 1, 0)
test_y <- ifelse(test_data$heart_attack_risk == "yes", 1, 0)
# Prepare features
train_x <- train_balanced[, -which(names(train_balanced) == "heart_attack_risk")]
test_x <- test_data[, -which(names(test_data) == "heart_attack_risk")]
train_x[] <- lapply(train_x, as.numeric)
test_x[] <- lapply(test_x, as.numeric)
# Scale
pre_proc <- preProcess(train_x, method = c("center", "scale"))
train_x <- predict(pre_proc, train_x)
test_x <- predict(pre_proc, test_x)
# Convert to matrix
train_x <- as.matrix(train_x)
test_x <- as.matrix(test_x)
focal_loss <- function(gamma = 2., alpha = 0.25) {
function(y_true, y_pred) {
epsilon <- k_epsilon()
y_pred <- k_clip(y_pred, epsilon, 1.0 - epsilon)
pt_1 <- tf$where(k_equal(y_true, 1), y_pred, k_ones_like(y_pred))
pt_0 <- tf$where(k_equal(y_true, 0), y_pred, k_zeros_like(y_pred))
return(-alpha * k_pow(1 - pt_1, gamma) * k_log(pt_1) -
(1 - alpha) * k_pow(pt_0, gamma) * k_log(1 - pt_0))
}
}
model2 <- keras_model_sequential() %>%
layer_dense(units = 128, activation = "relu", input_shape = ncol(train_x)) %>%
layer_batch_normalization() %>%
layer_dropout(0.4) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_batch_normalization() %>%
layer_dropout(0.3) %>%
layer_dense(units = 32, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
model2 %>% compile(
loss = focal_loss(gamma = 2, alpha = 0.25),
optimizer = optimizer_adam(learning_rate = 0.001),
metrics = c("accuracy")
)
history <- model2 %>% fit(
train_x, train_y,
epochs = 50,
batch_size = 32,
validation_split = 0.2,
verbose = 1
)
## Epoch 1/50
##
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## 524/524 [==============================] - 4s 4ms/step - loss: 0.0836 - accuracy: 0.4689 - val_loss: 0.0846 - val_accuracy: 0.8760
## Epoch 2/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0721 - accuracy: 0.4775 - val_loss: 0.0754 - val_accuracy: 0.9344
## Epoch 3/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0698 - accuracy: 0.4872 - val_loss: 0.0717 - val_accuracy: 0.9492
## Epoch 4/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0682 - accuracy: 0.4903 - val_loss: 0.0667 - val_accuracy: 0.9597
## Epoch 5/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0675 - accuracy: 0.5031 - val_loss: 0.0598 - val_accuracy: 0.9742
## Epoch 6/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0668 - accuracy: 0.5041 - val_loss: 0.0573 - val_accuracy: 0.9666
## Epoch 7/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0659 - accuracy: 0.5171 - val_loss: 0.0579 - val_accuracy: 0.9604
## Epoch 8/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0653 - accuracy: 0.5258 - val_loss: 0.0578 - val_accuracy: 0.9456
## Epoch 9/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0645 - accuracy: 0.5419 - val_loss: 0.0517 - val_accuracy: 0.9618
## Epoch 10/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0646 - accuracy: 0.5403 - val_loss: 0.0590 - val_accuracy: 0.9423
## Epoch 11/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0634 - accuracy: 0.5575 - val_loss: 0.0597 - val_accuracy: 0.9051
## Epoch 12/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0634 - accuracy: 0.5618 - val_loss: 0.0586 - val_accuracy: 0.9179
## Epoch 13/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0628 - accuracy: 0.5684 - val_loss: 0.0545 - val_accuracy: 0.9315
## Epoch 14/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0621 - accuracy: 0.5823 - val_loss: 0.0593 - val_accuracy: 0.9091
## Epoch 15/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0619 - accuracy: 0.5890 - val_loss: 0.0528 - val_accuracy: 0.9337
## Epoch 16/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0617 - accuracy: 0.5877 - val_loss: 0.0521 - val_accuracy: 0.9315
## Epoch 17/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0615 - accuracy: 0.5936 - val_loss: 0.0504 - val_accuracy: 0.9439
## Epoch 18/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0611 - accuracy: 0.6055 - val_loss: 0.0513 - val_accuracy: 0.9363
## Epoch 19/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0608 - accuracy: 0.6010 - val_loss: 0.0593 - val_accuracy: 0.8960
## Epoch 20/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0611 - accuracy: 0.5998 - val_loss: 0.0520 - val_accuracy: 0.9265
## Epoch 21/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0602 - accuracy: 0.6117 - val_loss: 0.0526 - val_accuracy: 0.9127
## Epoch 22/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0603 - accuracy: 0.6179 - val_loss: 0.0503 - val_accuracy: 0.9292
## Epoch 23/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0604 - accuracy: 0.6109 - val_loss: 0.0477 - val_accuracy: 0.9382
## Epoch 24/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0601 - accuracy: 0.6148 - val_loss: 0.0478 - val_accuracy: 0.9349
## Epoch 25/50
##
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## 524/524 [==============================] - 3s 5ms/step - loss: 0.0597 - accuracy: 0.6179 - val_loss: 0.0522 - val_accuracy: 0.9170
## Epoch 26/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0589 - accuracy: 0.6350 - val_loss: 0.0473 - val_accuracy: 0.9275
## Epoch 27/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0594 - accuracy: 0.6290 - val_loss: 0.0516 - val_accuracy: 0.9187
## Epoch 28/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0591 - accuracy: 0.6310 - val_loss: 0.0482 - val_accuracy: 0.9263
## Epoch 29/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0589 - accuracy: 0.6359 - val_loss: 0.0504 - val_accuracy: 0.9208
## Epoch 30/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0586 - accuracy: 0.6353 - val_loss: 0.0466 - val_accuracy: 0.9256
## Epoch 31/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0578 - accuracy: 0.6493 - val_loss: 0.0517 - val_accuracy: 0.9062
## Epoch 32/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0582 - accuracy: 0.6383 - val_loss: 0.0479 - val_accuracy: 0.9213
## Epoch 33/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0579 - accuracy: 0.6440 - val_loss: 0.0475 - val_accuracy: 0.9191
## Epoch 34/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0574 - accuracy: 0.6551 - val_loss: 0.0493 - val_accuracy: 0.9132
## Epoch 35/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0580 - accuracy: 0.6434 - val_loss: 0.0515 - val_accuracy: 0.9024
## Epoch 36/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0580 - accuracy: 0.6483 - val_loss: 0.0497 - val_accuracy: 0.9086
## Epoch 37/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0577 - accuracy: 0.6501 - val_loss: 0.0500 - val_accuracy: 0.9148
## Epoch 38/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0575 - accuracy: 0.6532 - val_loss: 0.0507 - val_accuracy: 0.9098
## Epoch 39/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0572 - accuracy: 0.6572 - val_loss: 0.0522 - val_accuracy: 0.8962
## Epoch 40/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0569 - accuracy: 0.6588 - val_loss: 0.0471 - val_accuracy: 0.9270
## Epoch 41/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0569 - accuracy: 0.6590 - val_loss: 0.0471 - val_accuracy: 0.9201
## Epoch 42/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0570 - accuracy: 0.6600 - val_loss: 0.0473 - val_accuracy: 0.9203
## Epoch 43/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0567 - accuracy: 0.6624 - val_loss: 0.0470 - val_accuracy: 0.9182
## Epoch 44/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0564 - accuracy: 0.6712 - val_loss: 0.0462 - val_accuracy: 0.9229
## Epoch 45/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0572 - accuracy: 0.6565 - val_loss: 0.0473 - val_accuracy: 0.9289
## Epoch 46/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0562 - accuracy: 0.6652 - val_loss: 0.0452 - val_accuracy: 0.9241
## Epoch 47/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.6686 - val_loss: 0.0454 - val_accuracy: 0.9244
## Epoch 48/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0563 - accuracy: 0.6723 - val_loss: 0.0495 - val_accuracy: 0.9043
## Epoch 49/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0562 - accuracy: 0.6697 - val_loss: 0.0446 - val_accuracy: 0.9339
## Epoch 50/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0557 - accuracy: 0.6745 - val_loss: 0.0449 - val_accuracy: 0.9227
# Predict
pred_probs_nn2 <- model2 %>% predict(test_x)
## 55/55 - 0s - 180ms/epoch - 3ms/step
pred_classes_nn2 <- ifelse(pred_probs_nn2 > 0.5, "yes", "no")
# Confusion matrix
confusion_matrix_nn2<- confusionMatrix(
factor(pred_classes_nn2, levels = c("no", "yes")),
factor(ifelse(test_y == 1, "yes", "no"), levels = c("no", "yes"))
)
print(confusion_matrix_nn2)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 616 452
## yes 257 427
##
## Accuracy : 0.5953
## 95% CI : (0.5719, 0.6184)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : 2.314e-15
##
## Kappa : 0.1912
##
## Mcnemar's Test P-Value : 3.197e-13
##
## Sensitivity : 0.7056
## Specificity : 0.4858
## Pos Pred Value : 0.5768
## Neg Pred Value : 0.6243
## Prevalence : 0.4983
## Detection Rate : 0.3516
## Detection Prevalence : 0.6096
## Balanced Accuracy : 0.5957
##
## 'Positive' Class : no
##
accuracy_nn2 <- confusion_matrix_nn2$overall['Accuracy']
precision_nn2 <- confusion_matrix_nn2$byClass['Pos Pred Value']
recall_nn2 <- confusion_matrix_nn2$byClass['Sensitivity']
f1_nn2 <- confusion_matrix_nn2$byClass['F1']
metrics_nns <- rbind(metrics_nns, data.frame(
Model = "NN scale+balance",
Accuracy = as.numeric(accuracy_nn2),
Precision = as.numeric(precision_nn2),
Recall = as.numeric(recall_nn2),
F1 = as.numeric(f1_nn2)
))
NN:Model 3
# Title: Optimized Neural Network (Scaled + SMOTE + Early Stopping)
# Description: Smaller architecture, early stopping to prevent overfitting
set.seed(105)
# Apply SMOTE to balance the training set
train_balanced <- SMOTE(heart_attack_risk ~ ., data = train_data, perc.over = 200, perc.under = 150)
train_y <- ifelse(train_balanced$heart_attack_risk == "yes", 1, 0)
test_y <- ifelse(test_data$heart_attack_risk == "yes", 1, 0)
# Prepare features
train_x <- train_balanced[, -which(names(train_balanced) == "heart_attack_risk")]
test_x <- test_data[, -which(names(test_data) == "heart_attack_risk")]
train_x[] <- lapply(train_x, as.numeric)
test_x[] <- lapply(test_x, as.numeric)
# Scale
pre_proc <- preProcess(train_x, method = c("center", "scale"))
train_x <- predict(pre_proc, train_x)
test_x <- predict(pre_proc, test_x)
train_x <- as.matrix(train_x)
test_x <- as.matrix(test_x)
focal_loss <- function(gamma = 2., alpha = 0.25) {
function(y_true, y_pred) {
epsilon <- k_epsilon()
y_pred <- k_clip(y_pred, epsilon, 1.0 - epsilon)
pt_1 <- tf$where(k_equal(y_true, 1), y_pred, k_ones_like(y_pred))
pt_0 <- tf$where(k_equal(y_true, 0), y_pred, k_zeros_like(y_pred))
return(-alpha * k_pow(1 - pt_1, gamma) * k_log(pt_1) -
(1 - alpha) * k_pow(pt_0, gamma) * k_log(1 - pt_0))
}
}
model3 <- keras_model_sequential() %>%
# Reduced units in first layer for speed
layer_dense(units = 64, activation = "relu", input_shape = ncol(train_x)) %>% # from 128 to 64
layer_batch_normalization() %>%
layer_dropout(0.3) %>% #from 0.4 to 0.3
layer_dense(units = 32, activation = "relu") %>% # from 64 to 32
layer_batch_normalization() %>%
layer_dropout(0.2) %>% # from 0.3 to 0.2
layer_dense(units = 16, activation = "relu") %>% # small layer to retain depth
layer_dense(units = 1, activation = "sigmoid")
model3 %>% compile(
loss = focal_loss(gamma = 2, alpha = 0.25),
optimizer = optimizer_adam(learning_rate = 0.001),
metrics = c("accuracy")
)
# Early stopping added to speed training without overfitting!!
early_stop <- callback_early_stopping(monitor = "val_loss", patience = 5, restore_best_weights = TRUE)
# Fit model
history <- model3 %>% fit(
train_x, train_y,
epochs = 50,
batch_size = 32,
validation_split = 0.2,
callbacks = list(early_stop),
verbose = 1
)
## Epoch 1/50
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## 524/524 [==============================] - 4s 4ms/step - loss: 0.0819 - accuracy: 0.4619 - val_loss: 0.0797 - val_accuracy: 0.9523
## Epoch 2/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0717 - accuracy: 0.4532 - val_loss: 0.0697 - val_accuracy: 0.9874
## Epoch 3/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0699 - accuracy: 0.4457 - val_loss: 0.0681 - val_accuracy: 0.9924
## Epoch 4/50
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0689 - accuracy: 0.4461 - val_loss: 0.0687 - val_accuracy: 0.9890
## Epoch 5/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0685 - accuracy: 0.4509 - val_loss: 0.0651 - val_accuracy: 0.9909
## Epoch 6/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0675 - accuracy: 0.4652 - val_loss: 0.0651 - val_accuracy: 0.9857
## Epoch 7/50
##
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## Epoch 8/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0663 - accuracy: 0.4862 - val_loss: 0.0595 - val_accuracy: 0.9828
## Epoch 9/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0658 - accuracy: 0.5003 - val_loss: 0.0556 - val_accuracy: 0.9843
## Epoch 10/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0655 - accuracy: 0.5041 - val_loss: 0.0588 - val_accuracy: 0.9623
## Epoch 11/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0649 - accuracy: 0.5190 - val_loss: 0.0589 - val_accuracy: 0.9642
## Epoch 12/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0649 - accuracy: 0.5189 - val_loss: 0.0572 - val_accuracy: 0.9688
## Epoch 13/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0644 - accuracy: 0.5292 - val_loss: 0.0556 - val_accuracy: 0.9578
## Epoch 14/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0640 - accuracy: 0.5410 - val_loss: 0.0561 - val_accuracy: 0.9523
## Epoch 15/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0639 - accuracy: 0.5469 - val_loss: 0.0554 - val_accuracy: 0.9652
## Epoch 16/50
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0631 - accuracy: 0.5584 - val_loss: 0.0516 - val_accuracy: 0.9609
## Epoch 17/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0630 - accuracy: 0.5613 - val_loss: 0.0550 - val_accuracy: 0.9454
## Epoch 18/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0632 - accuracy: 0.5579 - val_loss: 0.0557 - val_accuracy: 0.9404
## Epoch 19/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0623 - accuracy: 0.5830 - val_loss: 0.0525 - val_accuracy: 0.9461
## Epoch 20/50
##
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## 524/524 [==============================] - 2s 3ms/step - loss: 0.0624 - accuracy: 0.5741 - val_loss: 0.0517 - val_accuracy: 0.9439
## Epoch 21/50
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0623 - accuracy: 0.5759 - val_loss: 0.0523 - val_accuracy: 0.9416
# Predict
pred_probs_nn3 <- model3 %>% predict(test_x)
## 55/55 - 0s - 194ms/epoch - 4ms/step
pred_classes_nn3 <- ifelse(pred_probs_nn3 > 0.5, "yes", "no")
# Confusion matrix
confusion_matrix_nn3<- confusionMatrix(
factor(pred_classes_nn3, levels = c("no", "yes")),
factor(ifelse(test_y == 1, "yes", "no"), levels = c("no", "yes"))
)
print(confusion_matrix_nn3)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 677 608
## yes 196 271
##
## Accuracy : 0.5411
## 95% CI : (0.5174, 0.5646)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : 0.0005286
##
## Kappa : 0.0837
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.7755
## Specificity : 0.3083
## Pos Pred Value : 0.5268
## Neg Pred Value : 0.5803
## Prevalence : 0.4983
## Detection Rate : 0.3864
## Detection Prevalence : 0.7334
## Balanced Accuracy : 0.5419
##
## 'Positive' Class : no
##
accuracy_nn3 <- confusion_matrix_nn3$overall['Accuracy']
precision_nn3 <- confusion_matrix_nn3$byClass['Pos Pred Value']
recall_nn3 <- confusion_matrix_nn3$byClass['Sensitivity']
f1_nn3 <- confusion_matrix_nn3$byClass['F1']
metrics_nns <- rbind(metrics_nns, data.frame(
Model = "NN +early stopping",
Accuracy = as.numeric(accuracy_nn3),
Precision = as.numeric(precision_nn3),
Recall = as.numeric(recall_nn3),
F1 = as.numeric(f1_nn3)
))
NN:Model 4
set.seed(105)
# Accuracy-Optimized Neural Network (Scaled + SMOTE + BatchNorm + Dropout)
# Description: Larger architecture with batch normalization and increased dropout for better generalization
# Apply SMOTE to balance the training set
train_balanced <- SMOTE(heart_attack_risk ~ ., data = train_data, perc.over = 200, perc.under = 150)
train_y <- ifelse(train_balanced$heart_attack_risk == "yes", 1, 0)
test_y <- ifelse(test_data$heart_attack_risk == "yes", 1, 0)
# Prepare features
train_x <- train_balanced[, -which(names(train_balanced) == "heart_attack_risk")]
test_x <- test_data[, -which(names(test_data) == "heart_attack_risk")]
train_x[] <- lapply(train_x, as.numeric)
test_x[] <- lapply(test_x, as.numeric)
# Scale
pre_proc <- preProcess(train_x, method = c("center", "scale"))
train_x <- predict(pre_proc, train_x)
test_x <- predict(pre_proc, test_x)
# Convert to matrix
train_x <- as.matrix(train_x)
test_x <- as.matrix(test_x)
# Focal loss
focal_loss <- function(gamma = 2., alpha = 0.25) {
function(y_true, y_pred) {
epsilon <- k_epsilon()
y_pred <- k_clip(y_pred, epsilon, 1.0 - epsilon)
pt_1 <- tf$where(k_equal(y_true, 1), y_pred, k_ones_like(y_pred))
pt_0 <- tf$where(k_equal(y_true, 0), y_pred, k_zeros_like(y_pred))
return(-alpha * k_pow(1 - pt_1, gamma) * k_log(pt_1) -
(1 - alpha) * k_pow(pt_0, gamma) * k_log(1 - pt_0))
}
}
model4 <- keras_model_sequential() %>%
layer_dense(units = 128, activation = "relu", input_shape = ncol(train_x)) %>% # from 64 to 128
layer_batch_normalization() %>%
layer_dropout(0.4) %>%
layer_dense(units = 64, activation = "relu") %>%
layer_batch_normalization() %>%
layer_dropout(0.3) %>%
layer_dense(units = 32, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
# COMPILE
model4 %>% compile(
loss = focal_loss(gamma = 2, alpha = 0.25),
optimizer = optimizer_adam(learning_rate = 0.0005), # lowered LR for better convergence
metrics = c("accuracy")
)
# Early stopping
early_stop <- callback_early_stopping(monitor = "val_loss", patience = 7, restore_best_weights = TRUE) # CHANGED patience from 5 → 7
history <- model4 %>% fit(
train_x, train_y,
epochs = 60, # increased from 50 for more learning room but <100
batch_size = 32,
validation_split = 0.2,
callbacks = list(early_stop),
verbose = 1
)
## Epoch 1/60
##
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## 524/524 [==============================] - 4s 4ms/step - loss: 0.0973 - accuracy: 0.4780 - val_loss: 0.0747 - val_accuracy: 0.8991
## Epoch 2/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0782 - accuracy: 0.4845 - val_loss: 0.0723 - val_accuracy: 0.9377
## Epoch 3/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0743 - accuracy: 0.4795 - val_loss: 0.0675 - val_accuracy: 0.9614
## Epoch 4/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0714 - accuracy: 0.4866 - val_loss: 0.0704 - val_accuracy: 0.9485
## Epoch 5/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0699 - accuracy: 0.4940 - val_loss: 0.0653 - val_accuracy: 0.9585
## Epoch 6/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0693 - accuracy: 0.4857 - val_loss: 0.0623 - val_accuracy: 0.9649
## Epoch 7/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0680 - accuracy: 0.4953 - val_loss: 0.0615 - val_accuracy: 0.9656
## Epoch 8/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0678 - accuracy: 0.4927 - val_loss: 0.0621 - val_accuracy: 0.9621
## Epoch 9/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0667 - accuracy: 0.5044 - val_loss: 0.0582 - val_accuracy: 0.9740
## Epoch 10/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0660 - accuracy: 0.5100 - val_loss: 0.0573 - val_accuracy: 0.9649
## Epoch 11/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0658 - accuracy: 0.5236 - val_loss: 0.0540 - val_accuracy: 0.9735
## Epoch 12/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0653 - accuracy: 0.5216 - val_loss: 0.0567 - val_accuracy: 0.9568
## Epoch 13/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0649 - accuracy: 0.5295 - val_loss: 0.0566 - val_accuracy: 0.9492
## Epoch 14/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0645 - accuracy: 0.5349 - val_loss: 0.0564 - val_accuracy: 0.9456
## Epoch 15/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0640 - accuracy: 0.5467 - val_loss: 0.0578 - val_accuracy: 0.9385
## Epoch 16/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0637 - accuracy: 0.5517 - val_loss: 0.0536 - val_accuracy: 0.9437
## Epoch 17/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0635 - accuracy: 0.5596 - val_loss: 0.0522 - val_accuracy: 0.9466
## Epoch 18/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0634 - accuracy: 0.5607 - val_loss: 0.0564 - val_accuracy: 0.9396
## Epoch 19/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0626 - accuracy: 0.5810 - val_loss: 0.0488 - val_accuracy: 0.9606
## Epoch 20/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0628 - accuracy: 0.5695 - val_loss: 0.0466 - val_accuracy: 0.9628
## Epoch 21/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0618 - accuracy: 0.5866 - val_loss: 0.0498 - val_accuracy: 0.9416
## Epoch 22/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0620 - accuracy: 0.5831 - val_loss: 0.0509 - val_accuracy: 0.9439
## Epoch 23/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0617 - accuracy: 0.5876 - val_loss: 0.0485 - val_accuracy: 0.9413
## Epoch 24/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0616 - accuracy: 0.5907 - val_loss: 0.0498 - val_accuracy: 0.9349
## Epoch 25/60
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0611 - accuracy: 0.5974 - val_loss: 0.0519 - val_accuracy: 0.9294
## Epoch 26/60
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0608 - accuracy: 0.6044 - val_loss: 0.0525 - val_accuracy: 0.9203
## Epoch 27/60
##
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## 524/524 [==============================] - 2s 4ms/step - loss: 0.0613 - accuracy: 0.5995 - val_loss: 0.0481 - val_accuracy: 0.9370
# Predict
pred_probs_nn4 <- model4 %>% predict(test_x)
## 55/55 - 0s - 178ms/epoch - 3ms/step
pred_classes_nn4 <- ifelse(pred_probs_nn4 > 0.5, "yes", "no")
# Evaluate
confusion_matrix_nn4<-confusionMatrix(
factor(pred_classes_nn4, levels = c("no", "yes")),
factor(ifelse(test_y == 1, "yes", "no"), levels = c("no", "yes"))
)
print(confusion_matrix_nn4)
## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 693 605
## yes 180 274
##
## Accuracy : 0.5519
## 95% CI : (0.5283, 0.5754)
## No Information Rate : 0.5017
## P-Value [Acc > NIR] : 1.428e-05
##
## Kappa : 0.1054
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.7938
## Specificity : 0.3117
## Pos Pred Value : 0.5339
## Neg Pred Value : 0.6035
## Prevalence : 0.4983
## Detection Rate : 0.3955
## Detection Prevalence : 0.7409
## Balanced Accuracy : 0.5528
##
## 'Positive' Class : no
##
accuracy_nn4 <- confusion_matrix_nn4$overall['Accuracy']
precision_nn4 <- confusion_matrix_nn4$byClass['Pos Pred Value']
recall_nn4 <- confusion_matrix_nn4$byClass['Sensitivity']
f1_nn4 <- confusion_matrix_nn4$byClass['F1']
metrics_nns <- rbind(metrics_nns, data.frame(
Model = "NN +BatchNorm + dropout",
Accuracy = as.numeric(accuracy_nn4),
Precision = as.numeric(precision_nn4),
Recall = as.numeric(recall_nn4),
F1 = as.numeric(f1_nn4)
))
roc1 <- roc(test_y, pred_probs_nn1)
roc2 <- roc(test_y, pred_probs_nn2)
roc3 <- roc(test_y, pred_probs_nn3)
roc4 <- roc(test_y, pred_probs_nn4)
plot(roc1, col = "blue", legacy.axes = TRUE, main = "ROC Curve - NN Models")
lines(roc2, col = "green")
lines(roc3, col = "red")
lines(roc4, col = "orange")
legend("bottomright", legend = c("Model 1", "Model 2", "Model 3","Model 4"),
col = c("blue", "green", "red","orange"), lwd = 2)
## Metrics table
kable(metrics_nns, caption = "Neural Network Model Performance Metrics", digits = 2)
Neural Network Model Performance Metrics
| NN Base Model |
0.50 |
0.51 |
0.02 |
0.04 |
| NN scale+balance |
0.60 |
0.58 |
0.71 |
0.63 |
| NN +early stopping |
0.54 |
0.53 |
0.78 |
0.63 |
| NN +BatchNorm + dropout |
0.55 |
0.53 |
0.79 |
0.64 |
# Metrics Combined
metrics_combined <- bind_rows(metrics_rfs, metrics_nns)
metrics_combined <- metrics_combined %>% select(Model, everything())
# Create one kable table
kable(metrics_combined, caption = "Model Performance Metrics Comparison", digits = 2)
Model Performance Metrics Comparison
| RF Base Model |
0.66 |
0.66 |
0.65 |
0.66 |
| RF mtry=2 |
0.69 |
0.70 |
0.67 |
0.69 |
| RF mtry=4 |
0.65 |
0.65 |
0.64 |
0.64 |
| Random Forest mtry=2 & ntree=1000 |
0.70 |
0.70 |
0.69 |
0.70 |
| NN Base Model |
0.50 |
0.51 |
0.02 |
0.04 |
| NN scale+balance |
0.60 |
0.58 |
0.71 |
0.63 |
| NN +early stopping |
0.54 |
0.53 |
0.78 |
0.63 |
| NN +BatchNorm + dropout |
0.55 |
0.53 |
0.79 |
0.64 |