Teoria

El paquete caret (Clasification and Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automatico.

Instalar paquetes y llamar librerias

# install.packages("ggplot2") #Graficas con mejor diseno
library(ggplot2)
# install.packages("lattice") #Crear Graficos
library(lattice)
# install.packages("caret") # Algoritmos de aprendizaje automatico
library(caret)
# install.packages("datasets") # Usar la base de datos "Iris"
library(datasets)
# install.packages("DataExplorer") # Exploracion de datos
library(DataExplorer)
# install.packages("kernlab") # Paquete con metodos de aprendizaje automatico
library(kernlab)
## 
## Attaching package: 'kernlab'
## The following object is masked from 'package:ggplot2':
## 
##     alpha
# install.packages("randomForest") # Paquete para este metodo de clasificacion 
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
## 
## Attaching package: 'randomForest'
## The following object is masked from 'package:ggplot2':
## 
##     margin
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.3     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ kernlab::alpha()       masks ggplot2::alpha()
## ✖ dplyr::combine()       masks randomForest::combine()
## ✖ purrr::cross()         masks kernlab::cross()
## ✖ dplyr::filter()        masks stats::filter()
## ✖ dplyr::lag()           masks stats::lag()
## ✖ purrr::lift()          masks caret::lift()
## ✖ randomForest::margin() masks ggplot2::margin()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# file.choose()
# Cargar el dataset
heart_data <- read.csv("/Users/constantinomilletxacur/Desktop/Concentracion/Modulo 2/heart.csv")

# Convertir variables categóricas a factores
heart_data$sex <- as.factor(heart_data$sex)
heart_data$cp <- as.factor(heart_data$cp)
heart_data$fbs <- as.factor(heart_data$fbs)
heart_data$restecg <- as.factor(heart_data$restecg)
heart_data$exang <- as.factor(heart_data$exang)
heart_data$slope <- as.factor(heart_data$slope)
heart_data$ca <- as.factor(heart_data$ca)
heart_data$thal <- as.factor(heart_data$thal)
heart_data$target <- as.factor(heart_data$target)

Partir los Datos 80-20

set.seed(123)
trainIndex <- createDataPartition(heart_data$target, p = 0.8, list = FALSE)
heart_train <- heart_data[trainIndex, ]
heart_test <- heart_data[-trainIndex, ]

Modelos para Clasificación SVM (Máquina de Vectores de Soporte) SVM con Kernel Lineal

svm_linear_model <- train(target ~ ., data = heart_train, method = "svmLinear", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
svm_linear_train_predictions <- predict(svm_linear_model, newdata = heart_train)
mcre1 <- confusionMatrix(svm_linear_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
svm_linear_test_predictions <- predict(svm_linear_model, newdata = heart_test)
mcrp1 <- confusionMatrix(svm_linear_test_predictions, heart_test$target)

mcrp1
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 84  7
##          1 15 98
##                                           
##                Accuracy : 0.8922          
##                  95% CI : (0.8413, 0.9312)
##     No Information Rate : 0.5147          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.7836          
##                                           
##  Mcnemar's Test P-Value : 0.1356          
##                                           
##             Sensitivity : 0.8485          
##             Specificity : 0.9333          
##          Pos Pred Value : 0.9231          
##          Neg Pred Value : 0.8673          
##              Prevalence : 0.4853          
##          Detection Rate : 0.4118          
##    Detection Prevalence : 0.4461          
##       Balanced Accuracy : 0.8909          
##                                           
##        'Positive' Class : 0               
##

SVM con Kernel Radial

svm_radial_model <- train(target ~ ., data = heart_train, method = "svmRadial", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
svm_radial_train_predictions <- predict(svm_radial_model, newdata = heart_train)
mcre2 <- confusionMatrix(svm_radial_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
svm_radial_test_predictions <- predict(svm_radial_model, newdata = heart_test)
mcrp2 <- confusionMatrix(svm_radial_test_predictions, heart_test$target)

mcrp2
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 93  6
##          1  6 99
##                                           
##                Accuracy : 0.9412          
##                  95% CI : (0.8995, 0.9692)
##     No Information Rate : 0.5147          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.8823          
##                                           
##  Mcnemar's Test P-Value : 1               
##                                           
##             Sensitivity : 0.9394          
##             Specificity : 0.9429          
##          Pos Pred Value : 0.9394          
##          Neg Pred Value : 0.9429          
##              Prevalence : 0.4853          
##          Detection Rate : 0.4559          
##    Detection Prevalence : 0.4853          
##       Balanced Accuracy : 0.9411          
##                                           
##        'Positive' Class : 0               
##

SVM con Kernel Polinómico

svm_poly_model <- train(target ~ ., data = heart_train, method = "svmPoly", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
svm_poly_train_predictions <- predict(svm_poly_model, newdata = heart_train)
mcre3 <- confusionMatrix(svm_poly_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
svm_poly_test_predictions <- predict(svm_poly_model, newdata = heart_test)
mcrp3 <- confusionMatrix(svm_poly_test_predictions, heart_test$target)

mcrp3
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0  99   3
##          1   0 102
##                                          
##                Accuracy : 0.9853         
##                  95% CI : (0.9576, 0.997)
##     No Information Rate : 0.5147         
##     P-Value [Acc > NIR] : <2e-16         
##                                          
##                   Kappa : 0.9706         
##                                          
##  Mcnemar's Test P-Value : 0.2482         
##                                          
##             Sensitivity : 1.0000         
##             Specificity : 0.9714         
##          Pos Pred Value : 0.9706         
##          Neg Pred Value : 1.0000         
##              Prevalence : 0.4853         
##          Detection Rate : 0.4853         
##    Detection Prevalence : 0.5000         
##       Balanced Accuracy : 0.9857         
##                                          
##        'Positive' Class : 0              
##

Árbol de Decisión

rpart_model <- train(target ~ ., data = heart_train, method = "rpart", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
rpart_train_predictions <- predict(rpart_model, newdata = heart_train)
mcre4 <- confusionMatrix(rpart_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
rpart_test_predictions <- predict(rpart_model, newdata = heart_test)
mcrp4 <- confusionMatrix(rpart_test_predictions, heart_test$target)

mcrp4
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 74 14
##          1 25 91
##                                           
##                Accuracy : 0.8088          
##                  95% CI : (0.7481, 0.8604)
##     No Information Rate : 0.5147          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.6161          
##                                           
##  Mcnemar's Test P-Value : 0.1093          
##                                           
##             Sensitivity : 0.7475          
##             Specificity : 0.8667          
##          Pos Pred Value : 0.8409          
##          Neg Pred Value : 0.7845          
##              Prevalence : 0.4853          
##          Detection Rate : 0.3627          
##    Detection Prevalence : 0.4314          
##       Balanced Accuracy : 0.8071          
##                                           
##        'Positive' Class : 0               
##

Redes Neuronales

nnet_model <- train(target ~ ., data = heart_train, method = "nnet", trControl = trainControl(method = "cv", number = 5), trace = FALSE)

# Predicciones en el conjunto de entrenamiento
nnet_train_predictions <- predict(nnet_model, newdata = heart_train)
mcre5 <- confusionMatrix(nnet_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
nnet_test_predictions <- predict(nnet_model, newdata = heart_test)
mcrp5 <- confusionMatrix(nnet_test_predictions, heart_test$target)

mcrp5
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  0  1
##          0 92  7
##          1  7 98
##                                          
##                Accuracy : 0.9314         
##                  95% CI : (0.8875, 0.962)
##     No Information Rate : 0.5147         
##     P-Value [Acc > NIR] : <2e-16         
##                                          
##                   Kappa : 0.8626         
##                                          
##  Mcnemar's Test P-Value : 1              
##                                          
##             Sensitivity : 0.9293         
##             Specificity : 0.9333         
##          Pos Pred Value : 0.9293         
##          Neg Pred Value : 0.9333         
##              Prevalence : 0.4853         
##          Detection Rate : 0.4510         
##    Detection Prevalence : 0.4853         
##       Balanced Accuracy : 0.9313         
##                                          
##        'Positive' Class : 0              
##

Random Forest

rf_model <- train(target ~ ., data = heart_train, method = "rf", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
rf_train_predictions <- predict(rf_model, newdata = heart_train)
mcre6 <- confusionMatrix(rf_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
rf_test_predictions <- predict(rf_model, newdata = heart_test)
mcrp6 <- confusionMatrix(rf_test_predictions, heart_test$target)

mcrp6
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0  99   0
##          1   0 105
##                                      
##                Accuracy : 1          
##                  95% CI : (0.9821, 1)
##     No Information Rate : 0.5147     
##     P-Value [Acc > NIR] : < 2.2e-16  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
##                                      
##             Sensitivity : 1.0000     
##             Specificity : 1.0000     
##          Pos Pred Value : 1.0000     
##          Neg Pred Value : 1.0000     
##              Prevalence : 0.4853     
##          Detection Rate : 0.4853     
##    Detection Prevalence : 0.4853     
##       Balanced Accuracy : 1.0000     
##                                      
##        'Positive' Class : 0          
## 
resultados <- data.frame(
  "svmLinear"=c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "svmRadial"=c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "svmPoly"=c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "rpart"=c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "nnet"=c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "rf"=c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de Entrenamiento", "Precisión de Prueba")
resultados
##                            svmLinear svmRadial   svmPoly     rpart      nnet rf
## Precisión de Entrenamiento 0.8952497 0.9354446 1.0000000 0.7917174 0.9086480  1
## Precisión de Prueba        0.8921569 0.9411765 0.9852941 0.8088235 0.9313725  1

Conclusiones

Acorde al resumen de resultados, el modelo mejor evaluado es el de svmRadial.

---
title: "CARET HEART"
author: "Constantino Millet Xacur"
date: "2024-08-21"
output:
  html_document: 
    toc: true
    toc_float: true
    code_download: true
    theme: cerulean
---

![](/Users/constantinomilletxacur/Desktop/Concentracion/Modulo 2/Fotos m2/heart.jpg)


# <span style="color: violet;">Teoria</span>
El paquete *caret (Clasification and Regression Training)* es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automatico.

# <span style="color: violet;">Instalar paquetes y llamar librerias</span>
```{r}
# install.packages("ggplot2") #Graficas con mejor diseno
library(ggplot2)
# install.packages("lattice") #Crear Graficos
library(lattice)
# install.packages("caret") # Algoritmos de aprendizaje automatico
library(caret)
# install.packages("datasets") # Usar la base de datos "Iris"
library(datasets)
# install.packages("DataExplorer") # Exploracion de datos
library(DataExplorer)
# install.packages("kernlab") # Paquete con metodos de aprendizaje automatico
library(kernlab)
# install.packages("randomForest") # Paquete para este metodo de clasificacion 
library(randomForest)
# install.packages("tidyverse")
library(tidyverse)
```
```{r}
# file.choose()
```


```{r}
# Cargar el dataset
heart_data <- read.csv("/Users/constantinomilletxacur/Desktop/Concentracion/Modulo 2/heart.csv")

# Convertir variables categóricas a factores
heart_data$sex <- as.factor(heart_data$sex)
heart_data$cp <- as.factor(heart_data$cp)
heart_data$fbs <- as.factor(heart_data$fbs)
heart_data$restecg <- as.factor(heart_data$restecg)
heart_data$exang <- as.factor(heart_data$exang)
heart_data$slope <- as.factor(heart_data$slope)
heart_data$ca <- as.factor(heart_data$ca)
heart_data$thal <- as.factor(heart_data$thal)
heart_data$target <- as.factor(heart_data$target)

```

<span style="color: violet;">Partir los Datos 80-20</span>
```{r}
set.seed(123)
trainIndex <- createDataPartition(heart_data$target, p = 0.8, list = FALSE)
heart_train <- heart_data[trainIndex, ]
heart_test <- heart_data[-trainIndex, ]


```
<span style="color: violet;">Modelos para Clasificación</span>
<span style="color: violet;">SVM (Máquina de Vectores de Soporte)</span>
SVM con Kernel Lineal

```{r}
svm_linear_model <- train(target ~ ., data = heart_train, method = "svmLinear", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
svm_linear_train_predictions <- predict(svm_linear_model, newdata = heart_train)
mcre1 <- confusionMatrix(svm_linear_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
svm_linear_test_predictions <- predict(svm_linear_model, newdata = heart_test)
mcrp1 <- confusionMatrix(svm_linear_test_predictions, heart_test$target)

mcrp1



```

SVM con Kernel Radial
```{r}
svm_radial_model <- train(target ~ ., data = heart_train, method = "svmRadial", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
svm_radial_train_predictions <- predict(svm_radial_model, newdata = heart_train)
mcre2 <- confusionMatrix(svm_radial_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
svm_radial_test_predictions <- predict(svm_radial_model, newdata = heart_test)
mcrp2 <- confusionMatrix(svm_radial_test_predictions, heart_test$target)

mcrp2


```

SVM con Kernel Polinómico
```{r}
svm_poly_model <- train(target ~ ., data = heart_train, method = "svmPoly", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
svm_poly_train_predictions <- predict(svm_poly_model, newdata = heart_train)
mcre3 <- confusionMatrix(svm_poly_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
svm_poly_test_predictions <- predict(svm_poly_model, newdata = heart_test)
mcrp3 <- confusionMatrix(svm_poly_test_predictions, heart_test$target)

mcrp3

```

<span style="color: violet;">Árbol de Decisión</span>
```{r}
rpart_model <- train(target ~ ., data = heart_train, method = "rpart", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
rpart_train_predictions <- predict(rpart_model, newdata = heart_train)
mcre4 <- confusionMatrix(rpart_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
rpart_test_predictions <- predict(rpart_model, newdata = heart_test)
mcrp4 <- confusionMatrix(rpart_test_predictions, heart_test$target)

mcrp4


```

<span style="color: violet;">Redes Neuronales</span>
```{r}
nnet_model <- train(target ~ ., data = heart_train, method = "nnet", trControl = trainControl(method = "cv", number = 5), trace = FALSE)

# Predicciones en el conjunto de entrenamiento
nnet_train_predictions <- predict(nnet_model, newdata = heart_train)
mcre5 <- confusionMatrix(nnet_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
nnet_test_predictions <- predict(nnet_model, newdata = heart_test)
mcrp5 <- confusionMatrix(nnet_test_predictions, heart_test$target)

mcrp5


```

<span style="color: violet;">Random Forest</span>
```{r}
rf_model <- train(target ~ ., data = heart_train, method = "rf", trControl = trainControl(method = "cv", number = 5))

# Predicciones en el conjunto de entrenamiento
rf_train_predictions <- predict(rf_model, newdata = heart_train)
mcre6 <- confusionMatrix(rf_train_predictions, heart_train$target)

# Predicciones en el conjunto de prueba
rf_test_predictions <- predict(rf_model, newdata = heart_test)
mcrp6 <- confusionMatrix(rf_test_predictions, heart_test$target)

mcrp6


```
```{r}
resultados <- data.frame(
  "svmLinear"=c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "svmRadial"=c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "svmPoly"=c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "rpart"=c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "nnet"=c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "rf"=c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de Entrenamiento", "Precisión de Prueba")
resultados
```
# <span style="color: violet;">Conclusiones</span>

Acorde al resumen de resultados, el modelo mejor evaluado es el de **svmRadial**.

