Heart Regresion
Ricardo Canive Valencia A01236496
2026-02-19
#install.packages("caret")
library(caret)## Warning: package 'caret' was built under R version 4.5.2## Cargando paquete requerido: ggplot2## Cargando paquete requerido: lattice #install.packages("tidyverse")
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.2## Warning: package 'forcats' was built under R version 4.5.1## Warning: package 'lubridate' was built under R version 4.5.2## ── 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 1.9.5 ✔ tibble 3.2.1
## ✔ purrr 1.0.4 ✔ tidyr 1.3.1## ── 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\\sams\\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: 5nueva_persona <- data.frame(
age = 62,
sex = factor(0),
cp = factor(1),
thalach = 138,
oldpeak = 1.7
)
riesgo <- predict(modelo, newdata = nueva_persona, type = "response")
cbind(nueva_persona, Probabilidad_Enfermedad = riesgo)## age sex cp thalach oldpeak Probabilidad_Enfermedad
## 1 62 0 1 138 1.7 0.7159638#Conclusión Al revisar lo que arrojó el análisis, se puede notar que el modelo sí aporta información relevante para identificar la probabilidad de padecer enfermedad cardíaca. Varias de las variables incluidas tienen un peso importante dentro de la estimación, especialmente el tipo de dolor en el pecho, el sexo de la persona y el valor de oldpeak, que influyen de manera clara en el resultado final.