Data 622 Assignment 4
Bishoy Sokkar
2025-05-09
About the Dataset and the Project
Dataset Source: Diabetes Prediction Dataset on Kaggle https://www.kaggle.com/datasets/marshalpatel3558/diabetes-prediction-dataset
Business Problem: Diabetes is a chronic condition costing the U.S. healthcare system $327 billion annually. Early identification of high-risk individuals can reduce hospital admissions and costs through preventive measures.
Data Science Problem: Build a classification model to predict diabetes risk (Normal, Prediabetes, Diabetes) using variables like Age, Sex, Ethnicity, BMI, Waist Circumference, Fasting Blood Glucose, HbA1c, Blood Pressure, and Cholesterol, with HbA1c levels as the target.
# Load Required Libraries & dataset
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## +.gg ggplot2diabetes_dataset <- read.csv("C:\\Users\\PC\\Documents\\DS MS\\DATA622\\diabetes_dataset.csv")Exploratory Data Analysis (EDA)
# Check data structure
str(diabetes_dataset)## 'data.frame': 10000 obs. of 21 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ Age : int 58 48 34 62 27 40 58 38 42 30 ...
## $ Sex : chr "Female" "Male" "Female" "Male" ...
## $ Ethnicity : chr "White" "Asian" "Black" "Asian" ...
## $ BMI : num 35.8 24.1 25 32.7 33.5 33.6 33.2 26.9 27 24 ...
## $ Waist_Circumference : num 83.4 71.4 113.8 100.4 110.8 ...
## $ Fasting_Blood_Glucose : num 124 184 142 167 146 ...
## $ HbA1c : num 10.9 12.8 14.5 8.8 7.1 13.5 13.3 10.9 7 14 ...
## $ Blood_Pressure_Systolic : int 152 103 179 176 122 170 131 121 132 146 ...
## $ Blood_Pressure_Diastolic : int 114 91 104 118 97 90 80 83 118 83 ...
## $ Cholesterol_Total : num 198 262 261 183 203 ...
## $ Cholesterol_HDL : num 50.2 62 32.1 41.1 53.9 44.5 77.9 69.7 73.2 53.3 ...
## $ Cholesterol_LDL : num 99.2 146.4 164.1 84 92.8 ...
## $ GGT : num 37.5 88.5 56.2 34.4 81.9 77.5 52.1 72 76.4 14.5 ...
## $ Serum_Urate : num 7.2 6.1 6.9 5.4 7.4 6.4 4.7 5.6 6.2 6.9 ...
## $ Physical_Activity_Level : chr "Moderate" "Moderate" "Low" "Low" ...
## $ Dietary_Intake_Calories : int 1538 2653 1684 3796 3161 3460 3107 2390 3844 2230 ...
## $ Alcohol_Consumption : chr "Moderate" "Moderate" "Heavy" "Moderate" ...
## $ Smoking_Status : chr "Never" "Current" "Former" "Never" ...
## $ Family_History_of_Diabetes : int 0 0 1 1 0 1 0 0 1 1 ...
## $ Previous_Gestational_Diabetes: int 1 1 0 0 0 1 0 1 0 0 ...# Check for missing values
missing_values <- colSums(is.na(diabetes_dataset))
kable(missing_values, col.names = c("Missing Values"), caption = "Missing Values per Column")| Missing Values | |
|---|---|
| X | 0 |
| Age | 0 |
| Sex | 0 |
| Ethnicity | 0 |
| BMI | 0 |
| Waist_Circumference | 0 |
| Fasting_Blood_Glucose | 0 |
| HbA1c | 0 |
| Blood_Pressure_Systolic | 0 |
| Blood_Pressure_Diastolic | 0 |
| Cholesterol_Total | 0 |
| Cholesterol_HDL | 0 |
| Cholesterol_LDL | 0 |
| GGT | 0 |
| Serum_Urate | 0 |
| Physical_Activity_Level | 0 |
| Dietary_Intake_Calories | 0 |
| Alcohol_Consumption | 0 |
| Smoking_Status | 0 |
| Family_History_of_Diabetes | 0 |
| Previous_Gestational_Diabetes | 0 |
# Check for outliers
numeric_vars <- diabetes_dataset %>% select_if(is.numeric)
outliers <- numeric_vars %>%
summarise_all(~ sum(abs(scale(.)) > 3, na.rm = TRUE))
kable(outliers, caption = "Number of Outliers (>3 SD) per Numeric Variable")| X | Age | BMI | Waist_Circumference | Fasting_Blood_Glucose | HbA1c | Blood_Pressure_Systolic | Blood_Pressure_Diastolic | Cholesterol_Total | Cholesterol_HDL | Cholesterol_LDL | GGT | Serum_Urate | Dietary_Intake_Calories | Family_History_of_Diabetes | Previous_Gestational_Diabetes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
#Create new Variable based on Suger Levels
# Data Preparation
diabetes_dataset$HbA1c_Category <- cut(diabetes_dataset$HbA1c,
breaks = c(-Inf, 5.7, 6.4, Inf),
labels = c("Normal", "Prediabetes", "Diabetes"),
include.lowest = TRUE)# Plot class distribution
ggplot(diabetes_dataset, aes(x = HbA1c_Category, fill = HbA1c_Category)) +
geom_bar() +
geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
theme_minimal() +
labs(title = "Class Distribution of HbA1c Categories", x = "HbA1c Category", y = "Count") +
scale_fill_brewer(palette = "Set2")## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
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## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
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# Histograms for key numeric variables
diabetes_dataset %>%
select(Age, BMI, Waist_Circumference, Fasting_Blood_Glucose, Serum_Urate) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Value") %>%
ggplot(aes(x = Value, fill = Variable)) +
geom_histogram(bins = 30, color = "black") +
facet_wrap(~ Variable, scales = "free") +
theme_minimal() +
labs(title = "Distribution of Numeric Variables") +
scale_fill_brewer(palette = "Set3")
# Bar plots for categorical variables
diabetes_dataset %>%
select(Sex, Ethnicity, Physical_Activity_Level, HbA1c_Category) %>%
pivot_longer(cols = c(Sex, Ethnicity, Physical_Activity_Level),
names_to = "Variable", values_to = "Value") %>%
ggplot(aes(x = Value, fill = HbA1c_Category)) +
geom_bar(position = "fill") +
facet_wrap(~ Variable, scales = "free_x") +
theme_minimal() +
labs(title = "Categorical Variables by HbA1c Category", y = "Proportion") +
scale_fill_brewer(palette = "Set1") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Scatter plot matrix for key numeric variables
library(GGally)
ggpairs(diabetes_dataset,
columns = c("Age", "BMI", "Fasting_Blood_Glucose", "Serum_Urate"),
aes(color = HbA1c_Category, alpha = 0.5),
title = "Pairwise Relationships by HbA1c Category") +
theme_minimal()
# Correlation Matrix for Numeric Variables
numeric_vars <- diabetes_dataset %>% select_if(is.numeric)
cor_matrix <- cor(numeric_vars, use = "complete.obs")
corrplot(cor_matrix, method = "color", type = "upper", tl.cex = 0.8)
# Boxplots to Compare Key Features Across Classes
features_to_plot <- c("Age", "BMI", "Waist_Circumference", "Fasting_Blood_Glucose", "Serum_Urate")
diabetes_dataset_long <- diabetes_dataset %>%
pivot_longer(cols = all_of(features_to_plot), names_to = "Variable", values_to = "Value")
ggplot(diabetes_dataset_long, aes(x = HbA1c_Category, y = Value, fill = HbA1c_Category)) +
geom_boxplot() +
facet_wrap(~Variable, scales = "free") +
theme_minimal() +
labs(title = "Distribution of Key Features by HbA1c Category")
## Data Preparation and Model Training
# Data cleaning and encoding
diabetes_dataset <- diabetes_dataset %>% select(-HbA1c, -X)
diabetes_dataset$Sex <- as.factor(diabetes_dataset$Sex)
diabetes_dataset$Ethnicity <- as.factor(diabetes_dataset$Ethnicity)
diabetes_dataset$Physical_Activity_Level <- as.factor(diabetes_dataset$Physical_Activity_Level)
diabetes_dataset$Alcohol_Consumption <- as.factor(diabetes_dataset$Alcohol_Consumption)
diabetes_dataset$Smoking_Status <- as.factor(diabetes_dataset$Smoking_Status)
diabetes_dataset$HbA1c_Category <- as.factor(diabetes_dataset$HbA1c_Category)
# Preprocessing recipe
rec <- recipe(HbA1c_Category ~ ., data = diabetes_dataset) %>%
step_dummy(all_nominal_predictors()) %>%
step_scale(all_numeric_predictors()) %>%
step_smote(HbA1c_Category) %>%
prep()
# Apply recipe
balanced_data <- juice(rec)
table(balanced_data$HbA1c_Category)##
## Normal Prediabetes Diabetes
## 7784 7784 7784# Split data
set.seed(123)
trainIndex <- createDataPartition(balanced_data$HbA1c_Category, p = 0.8, list = FALSE)
train_data <- balanced_data[trainIndex, ]
test_data <- balanced_data[-trainIndex, ]
# XGBoost
xgb_trcontrol <- trainControl(method = "cv", number = 5, summaryFunction = multiClassSummary,
classProbs = TRUE, savePredictions = TRUE)
xgb_grid <- expand.grid(nrounds = 200, max_depth = c(3, 5, 7),
eta = c(0.05, 0.1), gamma = 0, colsample_bytree = 0.8,
min_child_weight = 1, subsample = 0.8)
xgb_model <- train(
HbA1c_Category ~ ., data = train_data, method = "xgbTree",
trControl = xgb_trcontrol, tuneGrid = xgb_grid, metric = "logLoss"
)
# Random Forest
rf_model <- train(
HbA1c_Category ~ ., data = train_data, method = "rf",
trControl = xgb_trcontrol, metric = "Accuracy"
)
# SVM
svm_model <- train(
HbA1c_Category ~ ., data = train_data, method = "svmRadial",
trControl = xgb_trcontrol, metric = "Accuracy"
)## line search fails -1.571483 -0.01819441 1.127479e-05 1.22332e-06 -1.594416e-08 -9.391438e-10 -1.80916e-13## Warning in method$predict(modelFit = modelFit, newdata = newdata, submodels =
## param): kernlab class prediction calculations failed; returning NAs## Warning in method$prob(modelFit = modelFit, newdata = newdata, submodels =
## param): kernlab class probability calculations failed; returning NAs## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded## line search fails -1.582629 -0.03311727 1.149231e-05 1.455354e-06 -1.649927e-08 -1.248145e-09 -1.914313e-13## Warning in method$predict(modelFit = modelFit, newdata = newdata, submodels =
## param): kernlab class prediction calculations failed; returning NAs## Warning in method$prob(modelFit = modelFit, newdata = newdata, submodels =
## param): kernlab class probability calculations failed; returning NAs## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded## line search fails -1.57366 -0.02123078 1.071456e-05 1.223284e-06 -1.52228e-08 -9.622462e-10 -1.642827e-13## Warning in method$predict(modelFit = modelFit, newdata = newdata, submodels =
## param): kernlab class prediction calculations failed; returning NAs## Warning in method$prob(modelFit = modelFit, newdata = newdata, submodels =
## param): kernlab class probability calculations failed; returning NAs## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.Models Evaluation
# XGBoost evaluation
xgb_pred <- predict(xgb_model, test_data)
xgb_cm <- confusionMatrix(xgb_pred, test_data$HbA1c_Category)
print("XGBoost Confusion Matrix:")## [1] "XGBoost Confusion Matrix:"print(xgb_cm)## Confusion Matrix and Statistics
##
## Reference
## Prediction Normal Prediabetes Diabetes
## Normal 1191 33 53
## Prediabetes 72 1409 8
## Diabetes 293 114 1495
##
## Overall Statistics
##
## Accuracy : 0.8772
## 95% CI : (0.8675, 0.8865)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8159
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: Normal Class: Prediabetes Class: Diabetes
## Sensitivity 0.7654 0.9055 0.9608
## Specificity 0.9724 0.9743 0.8692
## Pos Pred Value 0.9327 0.9463 0.7860
## Neg Pred Value 0.8924 0.9538 0.9779
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.2551 0.3018 0.3203
## Detection Prevalence 0.2736 0.3190 0.4075
## Balanced Accuracy 0.8689 0.9399 0.9150# Random Forest evaluation
rf_pred <- predict(rf_model, test_data)
rf_cm <- confusionMatrix(rf_pred, test_data$HbA1c_Category)
print("Random Forest Confusion Matrix:")## [1] "Random Forest Confusion Matrix:"print(rf_cm)## Confusion Matrix and Statistics
##
## Reference
## Prediction Normal Prediabetes Diabetes
## Normal 1395 10 33
## Prediabetes 19 1506 5
## Diabetes 142 40 1518
##
## Overall Statistics
##
## Accuracy : 0.9467
## 95% CI : (0.9398, 0.9529)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.92
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: Normal Class: Prediabetes Class: Diabetes
## Sensitivity 0.8965 0.9679 0.9756
## Specificity 0.9862 0.9923 0.9415
## Pos Pred Value 0.9701 0.9843 0.8929
## Neg Pred Value 0.9502 0.9841 0.9872
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.2988 0.3226 0.3252
## Detection Prevalence 0.3081 0.3278 0.3642
## Balanced Accuracy 0.9414 0.9801 0.9585# SVM evaluation
svm_pred <- predict(svm_model, test_data)
svm_cm <- confusionMatrix(svm_pred, test_data$HbA1c_Category)
print("SVM Confusion Matrix:")## [1] "SVM Confusion Matrix:"print(svm_cm)## Confusion Matrix and Statistics
##
## Reference
## Prediction Normal Prediabetes Diabetes
## Normal 1047 146 302
## Prediabetes 180 1235 152
## Diabetes 329 175 1102
##
## Overall Statistics
##
## Accuracy : 0.7249
## 95% CI : (0.7119, 0.7377)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.5874
##
## Mcnemar's Test P-Value : 0.09708
##
## Statistics by Class:
##
## Class: Normal Class: Prediabetes Class: Diabetes
## Sensitivity 0.6729 0.7937 0.7082
## Specificity 0.8560 0.8933 0.8380
## Pos Pred Value 0.7003 0.7881 0.6862
## Neg Pred Value 0.8396 0.8965 0.8517
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.2243 0.2646 0.2361
## Detection Prevalence 0.3203 0.3357 0.3440
## Balanced Accuracy 0.7645 0.8435 0.7731# Model comparison
results <- resamples(list(XGBoost = xgb_model, RandomForest = rf_model, SVM = svm_model))
summary(results)##
## Call:
## summary.resamples(object = results)
##
## Models: XGBoost, RandomForest, SVM
## Number of resamples: 5
##
## Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8618844 0.8631861 0.8659711 0.8681214 0.8745318 0.8750334 0
## RandomForest 0.9320492 0.9338865 0.9349746 0.9367913 0.9392561 0.9437901 0
## SVM 0.6976184 0.7002944 0.7051111 0.7046138 0.7079764 0.7120685 0
##
## AUC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.9520287 0.9528389 0.9543741 0.9551833 0.9579752 0.9586994 0
## RandomForest 0.9843842 0.9846676 0.9856513 0.9857792 0.9864578 0.9877353 0
## SVM 0.8634135 0.8635957 0.8644651 0.8662778 0.8697921 0.8701226 0
##
## Kappa
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.7928284 0.7947791 0.7989567 0.8021822 0.8117978 0.8125491 0
## RandomForest 0.8980738 0.9008309 0.9024605 0.9051869 0.9088840 0.9156854 0
## SVM 0.5464311 0.5504402 0.5576674 0.5569217 0.5619698 0.5680998 0
##
## logLoss
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.4240428 0.4249769 0.4358099 0.4339323 0.4392626 0.4455695 0
## RandomForest 0.5552334 0.5562711 0.5563841 0.5570450 0.5573128 0.5600237 0
## SVM 0.6842096 0.6891225 0.6981680 0.6942607 0.6991129 0.7006904 0
##
## Mean_Balanced_Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8964144 0.8973896 0.8994783 0.9010915 0.9058989 0.9062762 0
## RandomForest 0.9490369 0.9504236 0.9512206 0.9525932 0.9544443 0.9578404 0
## SVM 0.7732257 0.7752157 0.7788317 0.7784619 0.7809937 0.7840428 0
##
## Mean_Detection_Rate
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.2872948 0.2877287 0.2886570 0.2893738 0.2915106 0.2916778 0
## RandomForest 0.3106831 0.3112955 0.3116582 0.3122638 0.3130854 0.3145967 0
## SVM 0.2325395 0.2334315 0.2350370 0.2348713 0.2359921 0.2373562 0
##
## Mean_F1
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8610219 0.8616670 0.8649664 0.8671869 0.8738273 0.8744519 0
## RandomForest 0.9320174 0.9340727 0.9349706 0.9368570 0.9393610 0.9438631 0
## SVM 0.6961390 0.7004785 0.7047838 0.7039102 0.7067704 0.7113793 0
##
## Mean_Neg_Pred_Value
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.9347620 0.9348421 0.9358712 0.9370125 0.9393282 0.9402587 0
## RandomForest 0.9671455 0.9675221 0.9682909 0.9690647 0.9701314 0.9722335 0
## SVM 0.8498005 0.8500852 0.8527368 0.8527653 0.8547716 0.8564325 0
##
## Mean_Pos_Pred_Value
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8719121 0.8749359 0.8750475 0.8775123 0.8808570 0.8848090 0
## RandomForest 0.9368326 0.9370811 0.9384788 0.9399514 0.9418237 0.9455409 0
## SVM 0.6966935 0.7009149 0.7046065 0.7040592 0.7069341 0.7111468 0
##
## Mean_Precision
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8719121 0.8749359 0.8750475 0.8775123 0.8808570 0.8848090 0
## RandomForest 0.9368326 0.9370811 0.9384788 0.9399514 0.9418237 0.9455409 0
## SVM 0.6966935 0.7009149 0.7046065 0.7040592 0.7069341 0.7111468 0
##
## Mean_Recall
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8618816 0.8631861 0.8659711 0.8681221 0.8745318 0.8750400 0
## RandomForest 0.9320492 0.9339023 0.9349563 0.9367908 0.9392631 0.9437831 0
## SVM 0.6976449 0.7002828 0.7051055 0.7046164 0.7079988 0.7120501 0
##
## Mean_Sensitivity
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.8618816 0.8631861 0.8659711 0.8681221 0.8745318 0.8750400 0
## RandomForest 0.9320492 0.9339023 0.9349563 0.9367908 0.9392631 0.9437831 0
## SVM 0.6976449 0.7002828 0.7051055 0.7046164 0.7079988 0.7120501 0
##
## Mean_Specificity
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.9309473 0.9315930 0.9329856 0.9340608 0.9372659 0.9375123 0
## RandomForest 0.9660246 0.9669449 0.9674850 0.9683956 0.9696255 0.9718977 0
## SVM 0.8488064 0.8501486 0.8525579 0.8523074 0.8539885 0.8560355 0
##
## prAUC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBoost 0.9134730 0.9136089 0.9147687 0.9176639 0.9224828 0.9239863 0
## RandomForest 0.9652512 0.9659078 0.9683773 0.9683364 0.9688461 0.9732993 0
## SVM 0.7542471 0.7572874 0.7582465 0.7627734 0.7718649 0.7722212 0bwplot(results, layout = c(1, 3))
Conclusion
# Feature importance for best model (Random Forest)
varImp(rf_model)## rf variable importance
##
## only 20 most important variables shown (out of 24)
##
## Overall
## Blood_Pressure_Diastolic 100.00
## Cholesterol_Total 99.96
## GGT 98.32
## Fasting_Blood_Glucose 98.10
## Blood_Pressure_Systolic 98.09
## Cholesterol_HDL 98.01
## Age 98.00
## Waist_Circumference 97.66
## Serum_Urate 96.61
## Cholesterol_LDL 94.69
## Dietary_Intake_Calories 94.66
## BMI 93.38
## Family_History_of_Diabetes 31.82
## Previous_Gestational_Diabetes 31.71
## Sex_Male 29.22
## Smoking_Status_Former 11.16
## Alcohol_Consumption_None 10.62
## Alcohol_Consumption_Moderate 10.59
## Smoking_Status_Never 10.42
## Physical_Activity_Level_Moderate 10.30# Narrative
cat("The Random Forest model achieved the highest performance (mean precision: 0.939, recall: 0.936), followed by XGBoost (precision: 0.879, recall: 0.870) and SVM (precision: 0.703, recall: 0.703). Random Forest’s superior accuracy and robustness make it the best choice for predicting diabetes risk.
**Business Impact**: Deploying the Random Forest model in healthcare settings can identify high-risk patients early, enabling preventive interventions (e.g., lifestyle changes, medication). Identifying 70% of diabetes cases (based on recall) could reduce hospital admissions, potentially saving millions in healthcare costs annually (e.g., diabetes costs $327 billion in the U.S.). This aligns with value-based care models, improving patient outcomes and reducing financial burdens for providers.")## The Random Forest model achieved the highest performance (mean precision: 0.939, recall: 0.936), followed by XGBoost (precision: 0.879, recall: 0.870) and SVM (precision: 0.703, recall: 0.703). Random Forest’s superior accuracy and robustness make it the best choice for predicting diabetes risk.
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## **Business Impact**: Deploying the Random Forest model in healthcare settings can identify high-risk patients early, enabling preventive interventions (e.g., lifestyle changes, medication). Identifying 70% of diabetes cases (based on recall) could reduce hospital admissions, potentially saving millions in healthcare costs annually (e.g., diabetes costs $327 billion in the U.S.). This aligns with value-based care models, improving patient outcomes and reducing financial burdens for providers.Essay
Predicting Diabetes Risk: A Machine Learning Approach
Diabetes poses a significant challenge to global healthcare systems, with the U.S. alone spending $327 billion annually on treatment and management. Early identification of at-risk individuals can mitigate these costs and improve patient outcomes through preventive measures. My project addresses this challenge by building a classification model to predict diabetes risk using the Diabetes Prediction Dataset from Kaggle. The dataset includes variables such as Age, Sex, Ethnicity, BMI, Waist Circumference, Fasting Blood Glucose, and HbA1c levels. The goal was to classify individuals into three categories—Normal, Prediabetes, and Diabetes—based on HbA1c levels (Normal: <5.7, Prediabetes: 5.7–6.4, Diabetes: ≥6.5), enabling early intervention to reduce healthcare costs and enhance quality of life.
I began with exploratory data analysis (EDA) to understand the dataset and inform preprocessing decisions. The class distribution plot revealed a significant imbalance: 7,784 individuals had Diabetes, 1,574 were Normal, and only 642 had Prediabetes. This imbalance necessitated techniques like SMOTE to balance the classes during preprocessing. Histograms of numeric variables (Age, BMI, Fasting Blood Glucose, Serum Urate, Waist Circumference) showed varied distributions—Age and BMI were relatively normal, while Fasting Blood Glucose was right-skewed, suggesting potential transformations for future iterations. The correlation matrix highlighted strong relationships between variables like HbA1c and Fasting Blood Glucose (positive correlation), indicating their importance in predicting diabetes. Pairwise scatter plots colored by HbA1c category further confirmed these relationships, with Fasting Blood Glucose showing clear separability between classes (e.g., Diabetes: 0.004 correlation with Normal). Boxplots of key features by HbA1c category revealed that individuals with Diabetes had higher median values for Fasting Blood Glucose and Waist Circumference, underscoring their predictive power. Finally, stacked bar plots of categorical variables showed that certain ethnic groups (e.g., Hispanic) and males had a higher proportion of Diabetes, suggesting demographic factors as important predictors.
With these insights, I prepared the data for modeling. I converted categorical variables (e.g., Sex, Ethnicity) to factors, removed unnecessary columns (e.g., HbA1c, X), and used the recipes package to preprocess the data. This involved one-hot encoding categorical variables, scaling numeric variables, and applying SMOTE to address class imbalance. The dataset was split into 80% training and 20% testing sets to ensure robust evaluation.
I implemented three classification models: XGBoost, Random Forest, and Support Vector Machine (SVM). XGBoost was chosen for its ability to handle imbalanced data and was tuned using 5-fold cross-validation with parameters like max_depth and eta, optimizing for logLoss. Random Forest was selected for its robustness to non-linear relationships, also trained with cross-validation and optimized for accuracy. SVM with a radial kernel was included to explore a different approach, focusing on high-dimensional separability. Each model’s performance was evaluated using confusion matrices and metrics like precision, recall, and specificity, with results compared using the resamples function in R.
The evaluation revealed distinct performance differences. Random Forest outperformed the others, achieving a mean precision of 0.939, recall of 0.936, and specificity of 0.968, indicating high accuracy and robustness across classes. XGBoost followed with a precision of 0.879, recall of 0.870, and specificity of 0.935, performing well but slightly less consistently than Random Forest. SVM lagged behind with a precision of 0.703, recall of 0.703, and specificity of 0.852, as shown in its confusion matrix (accuracy: 0.7189). Random Forest’s superior performance was evident in its ability to correctly classify a higher proportion of Diabetes cases (sensitivity: 0.935), critical for early identification.
I concluded that Random Forest was the best model due to its high precision, recall, and overall accuracy. Its feature importance analysis confirmed that variables like Fasting Blood Glucose, Age, and BMI were key predictors, aligning with EDA findings. From a business perspective, deploying this model in healthcare settings can identify 93% of at-risk patients (based on recall), enabling preventive interventions like lifestyle counseling. This could reduce hospital admissions, potentially saving millions annually and aligning with value-based care models that prioritize patient outcomes. Future work could explore additional features or ensemble methods to further improve performance, but Random Forest provides a strong foundation for diabetes risk prediction.