Proyecto Final
Camila Palacios y Edinson Banguera
2024-06-07
## corrplot 0.92 loaded## Loading required package: MASS## Loading required package: visdat## Loading required package: car## Loading required package: carData## Loading required package: caret## Loading required package: ggplot2## Loading required package: lattice## Loading required package: pROC## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var## Loading required package: class## All packages loaded successfullyFallas cardiacas
(Proyecto Final)
Se trabajara con un dataset extraido de kaggle, del cual hace referencia a un paper en el que existe
## [1] "age" "anaemia"
## [3] "creatinine_phosphokinase" "diabetes"
## [5] "ejection_fraction" "high_blood_pressure"
## [7] "platelets" "serum_creatinine"
## [9] "serum_sodium" "sex"
## [11] "smoking" "time"
## [13] "DEATH_EVENT"## 'data.frame': 5000 obs. of 13 variables:
## $ Edad : num 55 65 45 60 95 70 63 70 50 53 ...
## $ Anemia : Factor w/ 2 levels "No","Yes": 1 1 1 2 2 1 2 2 1 2 ...
## $ Creatina : int 748 56 582 754 582 232 122 171 482 446 ...
## $ Diabetes : Factor w/ 2 levels "No","Yes": 1 1 2 2 1 2 2 1 2 1 ...
## $ EjecFrec : int 45 25 38 40 30 30 60 50 30 45 ...
## $ PresAlta : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 1 2 1 2 ...
## $ Plaquetas: num 263358 305000 319000 328000 461000 ...
## $ SerumC : num 1.3 5 0.9 1.2 2 1.2 1.2 0.9 0.9 1 ...
## $ SerumS : int 137 130 140 126 132 132 145 141 132 133 ...
## $ Genero : Factor w/ 2 levels "F","M": 2 2 1 2 2 2 1 1 2 2 ...
## $ Fumador : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 1 1 1 1 ...
## $ Tiempo : int 88 207 244 90 50 210 147 196 109 215 ...
## $ CausaM : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 1 1 1 ...## Edad Anemia Creatina Diabetes EjecFrec
## Min. :40.00 No :2628 Min. : 23.0 No :2803 Min. :14.00
## 1st Qu.:50.00 Yes:2372 1st Qu.: 121.0 Yes:2197 1st Qu.:30.00
## Median :60.00 Median : 248.0 Median :38.00
## Mean :60.29 Mean : 586.8 Mean :37.73
## 3rd Qu.:68.00 3rd Qu.: 582.0 3rd Qu.:45.00
## Max. :95.00 Max. :7861.0 Max. :80.00
## PresAlta Plaquetas SerumC SerumS Genero
## No :3176 Min. : 25100 Min. :0.500 Min. :113.0 F:1772
## Yes:1824 1st Qu.:215000 1st Qu.:0.900 1st Qu.:134.0 M:3228
## Median :263358 Median :1.100 Median :137.0
## Mean :265075 Mean :1.369 Mean :136.8
## 3rd Qu.:310000 3rd Qu.:1.400 3rd Qu.:140.0
## Max. :850000 Max. :9.400 Max. :148.0
## Fumador Tiempo CausaM
## No :3441 Min. : 4.0 No :3432
## Yes:1559 1st Qu.: 74.0 Yes:1568
## Median :113.0
## Mean :130.7
## 3rd Qu.:201.0
## Max. :285.0
## [[1]]
## [1] 0.7 1.9
##
## [[2]]
## [1] 0.7 1.9
##
## [[3]]
## [1] 0.7 1.9
##
## [[4]]
## [1] 0.7 1.9
##
## [[5]]
## [1] 0.7 1.9
##
## [[6]]
## [1] 0.7 1.9
Modelo Con dos Variables a Evaluar
##
## Call:
## glm(formula = CausaM ~ EjecFrec + SerumC, family = binomial,
## data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.439468 0.140592 3.126 0.00177 **
## EjecFrec -0.066428 0.003488 -19.046 < 2e-16 ***
## SerumC 0.843058 0.048138 17.513 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6219.5 on 4999 degrees of freedom
## Residual deviance: 5247.8 on 4997 degrees of freedom
## AIC: 5253.8
##
## Number of Fisher Scoring iterations: 5## Setting levels: control = No, case = Yes## Setting direction: controls < cases##
## Call:
## roc.default(response = data$CausaM, predictor = Prob_RL1, auc = TRUE, ci = TRUE)
##
## Data: Prob_RL1 in 3432 controls (data$CausaM No) < 1568 cases (data$CausaM Yes).
## Area under the curve: 0.7718
## 95% CI: 0.7565-0.7871 (DeLong)
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3239 977
## Yes 193 591
##
## Accuracy : 0.766
## 95% CI : (0.754, 0.7777)
## No Information Rate : 0.6864
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3711
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.3769
## Specificity : 0.9438
## Pos Pred Value : 0.7538
## Neg Pred Value : 0.7683
## Prevalence : 0.3136
## Detection Rate : 0.1182
## Detection Prevalence : 0.1568
## Balanced Accuracy : 0.6603
##
## 'Positive' Class : Yes
## Modelo Saturado y sin variables que no son significativas o que no aportan
##
## Call:
## glm(formula = CausaM ~ ., family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.132e+01 1.382e+00 8.194 2.53e-16 ***
## Edad 4.651e-02 3.912e-03 11.888 < 2e-16 ***
## AnemiaYes 9.193e-02 8.834e-02 1.041 0.29804
## Creatina 2.899e-04 4.654e-05 6.229 4.70e-10 ***
## DiabetesYes -4.953e-02 8.634e-02 -0.574 0.56619
## EjecFrec -7.734e-02 4.106e-03 -18.837 < 2e-16 ***
## PresAltaYes 2.462e-01 8.768e-02 2.808 0.00498 **
## Plaquetas -5.879e-07 4.440e-07 -1.324 0.18553
## SerumC 6.566e-01 4.714e-02 13.928 < 2e-16 ***
## SerumS -8.018e-02 9.804e-03 -8.178 2.87e-16 ***
## GeneroM 3.973e-03 1.002e-01 0.040 0.96838
## FumadorYes 5.482e-02 1.019e-01 0.538 0.59043
## Tiempo -2.042e-02 7.377e-04 -27.683 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6219.5 on 4999 degrees of freedom
## Residual deviance: 3601.6 on 4987 degrees of freedom
## AIC: 3627.6
##
## Number of Fisher Scoring iterations: 6## Setting levels: control = No, case = Yes## Setting direction: controls < cases##
## Call:
## roc.default(response = data$CausaM, predictor = Prob_RL2, auc = TRUE, ci = TRUE)
##
## Data: Prob_RL2 in 3432 controls (data$CausaM No) < 1568 cases (data$CausaM Yes).
## Area under the curve: 0.9008
## 95% CI: 0.8918-0.9097 (DeLong)
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3023 346
## Yes 409 1222
##
## Accuracy : 0.849
## 95% CI : (0.8388, 0.8588)
## No Information Rate : 0.6864
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.653
##
## Mcnemar's Test P-Value : 0.02404
##
## Sensitivity : 0.7793
## Specificity : 0.8808
## Pos Pred Value : 0.7492
## Neg Pred Value : 0.8973
## Prevalence : 0.3136
## Detection Rate : 0.2444
## Detection Prevalence : 0.3262
## Balanced Accuracy : 0.8301
##
## 'Positive' Class : Yes
## ## Start: AIC=3627.55
## CausaM ~ Edad + Anemia + Creatina + Diabetes + EjecFrec + PresAlta +
## Plaquetas + SerumC + SerumS + Genero + Fumador + Tiempo
##
## Df Deviance AIC
## - Genero 1 3601.6 3625.6
## - Fumador 1 3601.8 3625.8
## - Diabetes 1 3601.9 3625.9
## - Anemia 1 3602.6 3626.6
## - Plaquetas 1 3603.3 3627.3
## <none> 3601.6 3627.6
## - PresAlta 1 3609.4 3633.4
## - Creatina 1 3647.5 3671.5
## - SerumS 1 3669.7 3693.7
## - Edad 1 3754.1 3778.1
## - SerumC 1 3806.9 3830.9
## - EjecFrec 1 4033.4 4057.4
## - Tiempo 1 4755.6 4779.6
##
## Step: AIC=3625.55
## CausaM ~ Edad + Anemia + Creatina + Diabetes + EjecFrec + PresAlta +
## Plaquetas + SerumC + SerumS + Fumador + Tiempo
##
## Df Deviance AIC
## - Diabetes 1 3601.9 3623.9
## - Fumador 1 3601.9 3623.9
## - Anemia 1 3602.6 3624.6
## - Plaquetas 1 3603.3 3625.3
## <none> 3601.6 3625.6
## - PresAlta 1 3609.5 3631.5
## - Creatina 1 3648.5 3670.5
## - SerumS 1 3670.1 3692.1
## - Edad 1 3756.0 3778.0
## - SerumC 1 3806.9 3828.9
## - EjecFrec 1 4038.9 4060.9
## - Tiempo 1 4756.8 4778.8
##
## Step: AIC=3623.89
## CausaM ~ Edad + Anemia + Creatina + EjecFrec + PresAlta + Plaquetas +
## SerumC + SerumS + Fumador + Tiempo
##
## Df Deviance AIC
## - Fumador 1 3602.3 3622.3
## - Anemia 1 3602.9 3622.9
## - Plaquetas 1 3603.9 3623.9
## <none> 3601.9 3623.9
## - PresAlta 1 3610.0 3630.0
## - Creatina 1 3649.1 3669.1
## - SerumS 1 3670.2 3690.2
## - Edad 1 3759.5 3779.5
## - SerumC 1 3807.3 3827.3
## - EjecFrec 1 4039.1 4059.1
## - Tiempo 1 4756.9 4776.9
##
## Step: AIC=3622.3
## CausaM ~ Edad + Anemia + Creatina + EjecFrec + PresAlta + Plaquetas +
## SerumC + SerumS + Tiempo
##
## Df Deviance AIC
## - Anemia 1 3603.2 3621.2
## - Plaquetas 1 3604.1 3622.1
## <none> 3602.3 3622.3
## - PresAlta 1 3610.2 3628.2
## - Creatina 1 3649.3 3667.3
## - SerumS 1 3671.2 3689.2
## - Edad 1 3761.3 3779.3
## - SerumC 1 3809.1 3827.1
## - EjecFrec 1 4040.1 4058.1
## - Tiempo 1 4758.9 4776.9
##
## Step: AIC=3621.16
## CausaM ~ Edad + Creatina + EjecFrec + PresAlta + Plaquetas +
## SerumC + SerumS + Tiempo
##
## Df Deviance AIC
## - Plaquetas 1 3605.1 3621.1
## <none> 3603.2 3621.2
## - PresAlta 1 3611.4 3627.4
## - Creatina 1 3649.5 3665.5
## - SerumS 1 3671.3 3687.3
## - Edad 1 3766.5 3782.5
## - SerumC 1 3809.7 3825.7
## - EjecFrec 1 4041.7 4057.7
## - Tiempo 1 4779.2 4795.2
##
## Step: AIC=3621.14
## CausaM ~ Edad + Creatina + EjecFrec + PresAlta + SerumC + SerumS +
## Tiempo
##
## Df Deviance AIC
## <none> 3605.1 3621.1
## - PresAlta 1 3613.0 3627.0
## - Creatina 1 3650.9 3664.9
## - SerumS 1 3674.3 3688.3
## - Edad 1 3768.9 3782.9
## - SerumC 1 3812.4 3826.4
## - EjecFrec 1 4047.4 4061.4
## - Tiempo 1 4780.1 4794.1##
## Call:
## glm(formula = CausaM ~ Edad + Creatina + EjecFrec + PresAlta +
## SerumC + SerumS + Tiempo, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.112e+01 1.346e+00 8.261 < 2e-16 ***
## Edad 4.711e-02 3.834e-03 12.286 < 2e-16 ***
## Creatina 2.818e-04 4.554e-05 6.188 6.1e-10 ***
## EjecFrec -7.761e-02 4.077e-03 -19.036 < 2e-16 ***
## PresAltaYes 2.417e-01 8.636e-02 2.799 0.00512 **
## SerumC 6.569e-01 4.692e-02 13.999 < 2e-16 ***
## SerumS -7.967e-02 9.648e-03 -8.257 < 2e-16 ***
## Tiempo -2.042e-02 7.299e-04 -27.975 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6219.5 on 4999 degrees of freedom
## Residual deviance: 3605.1 on 4992 degrees of freedom
## AIC: 3621.1
##
## Number of Fisher Scoring iterations: 6##
## Call:
## glm(formula = CausaM ~ Edad + Creatina + EjecFrec + PresAlta +
## SerumC + SerumS + Tiempo, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.112e+01 1.346e+00 8.261 < 2e-16 ***
## Edad 4.711e-02 3.834e-03 12.286 < 2e-16 ***
## Creatina 2.818e-04 4.554e-05 6.188 6.1e-10 ***
## EjecFrec -7.761e-02 4.077e-03 -19.036 < 2e-16 ***
## PresAltaYes 2.417e-01 8.636e-02 2.799 0.00512 **
## SerumC 6.569e-01 4.692e-02 13.999 < 2e-16 ***
## SerumS -7.967e-02 9.648e-03 -8.257 < 2e-16 ***
## Tiempo -2.042e-02 7.299e-04 -27.975 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6219.5 on 4999 degrees of freedom
## Residual deviance: 3605.1 on 4992 degrees of freedom
## AIC: 3621.1
##
## Number of Fisher Scoring iterations: 6## Setting levels: control = No, case = Yes## Setting direction: controls < cases##
## Call:
## roc.default(response = data$CausaM, predictor = Prob_RL3, auc = TRUE, ci = TRUE)
##
## Data: Prob_RL3 in 3432 controls (data$CausaM No) < 1568 cases (data$CausaM Yes).
## Area under the curve: 0.901
## 95% CI: 0.8921-0.9099 (DeLong)
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3023 351
## Yes 409 1217
##
## Accuracy : 0.848
## 95% CI : (0.8377, 0.8578)
## No Information Rate : 0.6864
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.6504
##
## Mcnemar's Test P-Value : 0.03868
##
## Sensitivity : 0.7761
## Specificity : 0.8808
## Pos Pred Value : 0.7485
## Neg Pred Value : 0.8960
## Prevalence : 0.3136
## Detection Rate : 0.2434
## Detection Prevalence : 0.3252
## Balanced Accuracy : 0.8285
##
## 'Positive' Class : Yes
## ## Analysis of Deviance Table
##
## Model 1: CausaM ~ Edad + Anemia + Creatina + Diabetes + EjecFrec + PresAlta +
## Plaquetas + SerumC + SerumS + Genero + Fumador + Tiempo
## Model 2: CausaM ~ Edad + Creatina + EjecFrec + PresAlta + SerumC + SerumS +
## Tiempo
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 4987 3601.6
## 2 4992 3605.1 -5 -3.5848 0.6106##
## Call:
## glm(formula = CausaM ~ Edad + Creatina + EjecFrec + SerumC +
## SerumS + Tiempo, family = "binomial", data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.106e+01 1.347e+00 8.209 2.22e-16 ***
## Edad 4.731e-02 3.828e-03 12.359 < 2e-16 ***
## Creatina 2.748e-04 4.516e-05 6.085 1.16e-09 ***
## EjecFrec -7.764e-02 4.076e-03 -19.049 < 2e-16 ***
## SerumC 6.465e-01 4.726e-02 13.680 < 2e-16 ***
## SerumS -7.829e-02 9.638e-03 -8.123 4.55e-16 ***
## Tiempo -2.065e-02 7.265e-04 -28.431 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6219.5 on 4999 degrees of freedom
## Residual deviance: 3613.0 on 4993 degrees of freedom
## AIC: 3627
##
## Number of Fisher Scoring iterations: 6## Setting levels: control = No, case = Yes## Setting direction: controls < cases##
## Call:
## roc.default(response = data$CausaM, predictor = Prob_RL4, auc = TRUE, ci = TRUE)
##
## Data: Prob_RL4 in 3432 controls (data$CausaM No) < 1568 cases (data$CausaM Yes).
## Area under the curve: 0.9007
## 95% CI: 0.8918-0.9096 (DeLong)
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3045 380
## Yes 387 1188
##
## Accuracy : 0.8466
## 95% CI : (0.8363, 0.8565)
## No Information Rate : 0.6864
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.6441
##
## Mcnemar's Test P-Value : 0.8285
##
## Sensitivity : 0.7577
## Specificity : 0.8872
## Pos Pred Value : 0.7543
## Neg Pred Value : 0.8891
## Prevalence : 0.3136
## Detection Rate : 0.2376
## Detection Prevalence : 0.3150
## Balanced Accuracy : 0.8224
##
## 'Positive' Class : Yes
## ## AIC deviance R2
## Modelo_2Var 5253.775 5247.775 0.1562396
## Modelo_Sat 3627.552 3601.552 0.4209266
## Modelo_Fin 3621.137 3605.137 0.4203502
## Modelo_Fin2 3626.955 3612.955 0.4190932Evaluacion del modelo que no tiene las variables que no aportan