# install.packages("caret")
library(caret)## Loading required package: ggplot2## Loading required package: lattice# install.packages("tidyverse")
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ lubridate 1.9.5 ✔ tibble 3.3.1
## ✔ purrr 1.2.1 ✔ tidyr 1.3.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdf <- read.csv("C:\\Users\\karla\\Desktop\\CONCENTRACION\\Modulo_progra\\heart.csv")summary(df)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.0000 Min. : 94.0
## 1st Qu.:48.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:120.0
## Median :56.00 Median :1.0000 Median :1.0000 Median :130.0
## Mean :54.43 Mean :0.6956 Mean :0.9424 Mean :131.6
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.0000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.0000 Max. :200.0
## chol fbs restecg thalach
## Min. :126 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:132.0
## Median :240 Median :0.0000 Median :1.0000 Median :152.0
## Mean :246 Mean :0.1493 Mean :0.5298 Mean :149.1
## 3rd Qu.:275 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564 Max. :1.0000 Max. :2.0000 Max. :202.0
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.800 Median :1.000 Median :0.0000
## Mean :0.3366 Mean :1.072 Mean :1.385 Mean :0.7541
## 3rd Qu.:1.0000 3rd Qu.:1.800 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.200 Max. :2.000 Max. :4.0000
## thal target
## Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000
## Mean :2.324 Mean :0.5132
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000str(df)## 'data.frame': 1025 obs. of 14 variables:
## $ age : int 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : int 1 1 1 1 0 0 1 1 1 1 ...
## $ cp : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trestbps: int 125 140 145 148 138 100 114 160 120 122 ...
## $ chol : int 212 203 174 203 294 248 318 289 249 286 ...
## $ fbs : int 0 1 0 0 1 0 0 0 0 0 ...
## $ restecg : int 1 0 1 1 1 0 2 0 0 0 ...
## $ thalach : int 168 155 125 161 106 122 140 145 144 116 ...
## $ exang : int 0 1 1 0 0 0 0 1 0 1 ...
## $ oldpeak : num 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...
## $ slope : int 2 0 0 2 1 1 0 1 2 1 ...
## $ ca : int 2 0 0 1 3 0 3 1 0 2 ...
## $ thal : int 3 3 3 3 2 2 1 3 3 2 ...
## $ target : int 0 0 0 0 0 1 0 0 0 0 ...head(df)## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 2 53 1 0 140 203 1 0 155 1 3.1 0 0 3
## 3 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 4 61 1 0 148 203 0 1 161 0 0.0 2 1 3
## 5 62 0 0 138 294 1 1 106 0 1.9 1 3 2
## 6 58 0 0 100 248 0 0 122 0 1.0 1 0 2
## target
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 1summary(df)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.0000 Min. : 94.0
## 1st Qu.:48.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:120.0
## Median :56.00 Median :1.0000 Median :1.0000 Median :130.0
## Mean :54.43 Mean :0.6956 Mean :0.9424 Mean :131.6
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.0000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.0000 Max. :200.0
## chol fbs restecg thalach
## Min. :126 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:132.0
## Median :240 Median :0.0000 Median :1.0000 Median :152.0
## Mean :246 Mean :0.1493 Mean :0.5298 Mean :149.1
## 3rd Qu.:275 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564 Max. :1.0000 Max. :2.0000 Max. :202.0
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.800 Median :1.000 Median :0.0000
## Mean :0.3366 Mean :1.072 Mean :1.385 Mean :0.7541
## 3rd Qu.:1.0000 3rd Qu.:1.800 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.200 Max. :2.000 Max. :4.0000
## thal target
## Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000
## Mean :2.324 Mean :0.5132
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000str(df)## 'data.frame': 1025 obs. of 14 variables:
## $ age : int 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : int 1 1 1 1 0 0 1 1 1 1 ...
## $ cp : int 0 0 0 0 0 0 0 0 0 0 ...
## $ trestbps: int 125 140 145 148 138 100 114 160 120 122 ...
## $ chol : int 212 203 174 203 294 248 318 289 249 286 ...
## $ fbs : int 0 1 0 0 1 0 0 0 0 0 ...
## $ restecg : int 1 0 1 1 1 0 2 0 0 0 ...
## $ thalach : int 168 155 125 161 106 122 140 145 144 116 ...
## $ exang : int 0 1 1 0 0 0 0 1 0 1 ...
## $ oldpeak : num 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...
## $ slope : int 2 0 0 2 1 1 0 1 2 1 ...
## $ ca : int 2 0 0 1 3 0 3 1 0 2 ...
## $ thal : int 3 3 3 3 2 2 1 3 3 2 ...
## $ target : int 0 0 0 0 0 1 0 0 0 0 ...df <- df[, c("age","sex","cp","trestbps","chol","fbs",
"restecg","thalach","exang","oldpeak",
"slope","ca","thal","target")]
df <- na.omit(df)
df$target <- as.factor(df$target)
df$sex <- as.factor(df$sex)
df$fbs <- as.factor(df$fbs)
df$cp <- as.factor(df$cp)
df$thal <- as.factor(df$thal)
df$slope <- as.factor(df$slope)
df$exang <- as.factor(df$exang)
df$restecg <- as.factor(df$restecg)
df$ca <- as.factor(df$ca)modelo <- glm(target ~ ., data=df, family=binomial)
summary(modelo)##
## Call:
## glm(formula = target ~ ., family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.081901 2.028691 -0.040 0.967797
## age 0.026846 0.013950 1.924 0.054297 .
## sex1 -1.992347 0.314204 -6.341 2.28e-10 ***
## cp1 0.886380 0.308803 2.870 0.004100 **
## cp2 2.006394 0.286281 7.008 2.41e-12 ***
## cp3 2.409722 0.391965 6.148 7.86e-10 ***
## trestbps -0.024979 0.006537 -3.821 0.000133 ***
## chol -0.005462 0.002307 -2.367 0.017914 *
## fbs1 0.380096 0.319620 1.189 0.234356
## restecg1 0.397268 0.217975 1.823 0.068374 .
## restecg2 -0.800417 1.536998 -0.521 0.602530
## thalach 0.021692 0.006525 3.324 0.000886 ***
## exang1 -0.750331 0.248746 -3.016 0.002557 **
## oldpeak -0.403411 0.132156 -3.053 0.002269 **
## slope1 -0.595618 0.472076 -1.262 0.207057
## slope2 0.799689 0.504500 1.585 0.112941
## ca1 -2.334076 0.286781 -8.139 3.99e-16 ***
## ca2 -3.597039 0.444870 -8.086 6.19e-16 ***
## ca3 -2.288131 0.532138 -4.300 1.71e-05 ***
## ca4 1.565677 0.930256 1.683 0.092363 .
## thal1 2.796813 1.466219 1.908 0.056456 .
## thal2 2.404646 1.421542 1.692 0.090727 .
## thal3 0.991243 1.423972 0.696 0.486359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1420.24 on 1024 degrees of freedom
## Residual deviance: 606.82 on 1002 degrees of freedom
## AIC: 652.82
##
## Number of Fisher Scoring iterations: 6prueba <- df[c(5, 20), ]
probabilidad <- predict(modelo, newdata = prueba, type = "response")
cbind(prueba, Probabilidad_Enfermedad = probabilidad)## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 5 62 0 0 138 294 1 1 106 0 1.9 1 3 2
## 20 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## target Probabilidad_Enfermedad
## 5 0 0.1625751
## 20 1 0.9820719El modelo muestra que existen variables clínicamente relevantes que influyen significativamente en la probabilidad de padecer enfermedad cardíaca. Entre los factores más importantes se encuentran ca, cp, thalach, exang y oldpeak.
Al probar el modelo con dos pacientes reales del dataset, se observa una clara diferencia en la probabilidad estimada: 1. El primer paciente presenta una probabilidad de enfermedad de aproximadamente 16%, lo que indica bajo riesgo. 2. El segundo paciente muestra una probabilidad cercana al 98%, lo que indica un riesgo muy alto de enfermedad cardíaca.
En conclusión, el modelo logra diferenciar adecuadamente entre perfiles de bajo y alto riesgo, demostrando que las variables clínicas incluidas tienen un impacto significativo en la predicción de enfermedad cardíaca.