library(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(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\\ferpa\\Downloads\\heart.csv")# Explorar datos
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 ...df <- df[, c("target","age","sex","cp","thalach","oldpeak")]
# Eliminar valores faltantes
df <- na.omit(df)
# Convertir variable objetivo a factor
df$target <- as.factor(df$target)
# Convertir variables categóricas
df$sex <- as.factor(df$sex)
df$cp <- as.factor(df$cp)
str(df)## 'data.frame': 1025 obs. of 6 variables:
## $ target : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
## $ age : int 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 2 2 2 2 ...
## $ cp : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ thalach: int 168 155 125 161 106 122 140 145 144 116 ...
## $ oldpeak: num 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...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) -0.359747 1.056669 -0.340 0.733515
## age -0.039298 0.010644 -3.692 0.000222 ***
## sex1 -1.844887 0.208584 -8.845 < 2e-16 ***
## cp1 1.732941 0.256082 6.767 1.31e-11 ***
## cp2 2.204962 0.212669 10.368 < 2e-16 ***
## cp3 2.288578 0.314506 7.277 3.42e-13 ***
## thalach 0.023697 0.004608 5.143 2.71e-07 ***
## oldpeak -0.754388 0.092440 -8.161 3.33e-16 ***
## ---
## 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: 856.15 on 1017 degrees of freedom
## AIC: 872.15
##
## Number of Fisher Scoring iterations: 5prueba <- data.frame(
age = 35,
sex = as.factor(0),
cp = as.factor(1),
thalach = 150,
oldpeak = 2.4
)
probabilidad <- predict(modelo, newdata=prueba, type="response")
cbind(prueba, Probabilidad_Enfermedad = probabilidad)## age sex cp thalach oldpeak Probabilidad_Enfermedad
## 1 35 0 1 150 2.4 0.850923Para el caso de prueba (35 años, sexo=0, cp=1, thalach=150, oldpeak=2.4), el modelo estima una probabilidad de enfermedad cardíaca de 0.8509 (≈85.1%), lo que indica un riesgo alto según el modelo (muy por encima de un umbral típico de 0.50).