R Notebook

install.packages("RWeka")

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install.packages("caret")

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install.packages("ROCR")

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install.packages("rpart")

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install.packages("rpart.plot")

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install.packages("rattle")

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install.packages("RGtk2")

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suppressWarnings (library(RWeka))
suppressWarnings (library(caret))
suppressWarnings (library(ROCR))
# libraries for partition trees
suppressWarnings (library(rpart))
suppressWarnings (library(rpart.plot))
suppressWarnings (library(rattle))

Chronic_Kidney_Disease 

Chronic_Kidney_Disease[1:2,]

summary(Chronic_Kidney_Disease)

       id              age              bp               sg       
 Min.   :  0.00   Min.   : 2.00   Min.   : 50.00   Min.   :1.005  
 1st Qu.: 99.75   1st Qu.:42.00   1st Qu.: 70.00   1st Qu.:1.010  
 Median :199.50   Median :55.00   Median : 80.00   Median :1.020  
 Mean   :199.50   Mean   :51.48   Mean   : 76.47   Mean   :1.017  
 3rd Qu.:299.25   3rd Qu.:64.50   3rd Qu.: 80.00   3rd Qu.:1.020  
 Max.   :399.00   Max.   :90.00   Max.   :180.00   Max.   :1.025  
                  NA's   :9       NA's   :12       NA's   :47     
       al              su             rbc                 pc           
 Min.   :0.000   Min.   :0.0000   Length:400         Length:400        
 1st Qu.:0.000   1st Qu.:0.0000   Class :character   Class :character  
 Median :0.000   Median :0.0000   Mode  :character   Mode  :character  
 Mean   :1.017   Mean   :0.4501                                        
 3rd Qu.:2.000   3rd Qu.:0.0000                                        
 Max.   :5.000   Max.   :5.0000                                        
 NA's   :46      NA's   :49                                            
     pcc                 ba                 bgr            bu        
 Length:400         Length:400         Min.   : 22   Min.   :  1.50  
 Class :character   Class :character   1st Qu.: 99   1st Qu.: 27.00  
 Mode  :character   Mode  :character   Median :121   Median : 42.00  
                                       Mean   :148   Mean   : 57.43  
                                       3rd Qu.:163   3rd Qu.: 66.00  
                                       Max.   :490   Max.   :391.00  
                                       NA's   :44    NA's   :19      
       sc              sod             pot              hemo      
 Min.   : 0.400   Min.   :  4.5   Min.   : 2.500   Min.   : 3.10  
 1st Qu.: 0.900   1st Qu.:135.0   1st Qu.: 3.800   1st Qu.:10.30  
 Median : 1.300   Median :138.0   Median : 4.400   Median :12.65  
 Mean   : 3.072   Mean   :137.5   Mean   : 4.627   Mean   :12.53  
 3rd Qu.: 2.800   3rd Qu.:142.0   3rd Qu.: 4.900   3rd Qu.:15.00  
 Max.   :76.000   Max.   :163.0   Max.   :47.000   Max.   :17.80  
 NA's   :17       NA's   :87      NA's   :88       NA's   :52     
     pcv                 wc                 rc           
 Length:400         Length:400         Length:400        
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
                                                         
     htn                 dm                cad           
 Length:400         Length:400         Length:400        
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
                                                         
    appet                pe                ane           
 Length:400         Length:400         Length:400        
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
                                                         
 classification    
 Length:400        
 Class :character  
 Mode  :character  
                   
                   
                   
                   

 #% data missing in each column
apply(Chronic_Kidney_Disease,2,function(i) {(sum(is.na(i))/nrow(Chronic_Kidney_Disease))*100})

            id            age             bp             sg             al 
          0.00           2.25           3.00          11.75          11.50 
            su            rbc             pc            pcc             ba 
         12.25          38.00          16.25           1.00           1.00 
           bgr             bu             sc            sod            pot 
         11.00           4.75           4.25          21.75          22.00 
          hemo            pcv             wc             rc            htn 
         13.00          17.50          26.25          32.50           0.50 
            dm            cad          appet             pe            ane 
          0.50           0.50           0.25           0.25           0.25 
classification 
          0.00 

# samples with no missing data
sum(complete.cases(Chronic_Kidney_Disease))

[1] 158

# samples with no missing data after removing columns which have more than 25% of data missing
sum(complete.cases(Chronic_Kidney_Disease[,-c(6,17,18)]))

[1] 159

### remove rows with any missing value
Chronic_Kidney_Disease2 <- Chronic_Kidney_Disease[complete.cases(Chronic_Kidney_Disease),]
dim(Chronic_Kidney_Disease2)

[1] 158  26

apply(Chronic_Kidney_Disease2[,c(3:9,19:25)],2,table)

$bp

 50  60  70  80  90 100 110 
  1  40  37  63   9   7   1 

$sg

1.005 1.010 1.015 1.020 1.025 
    3    23    10    61    61 

$al

  0   1   2   3   4 
116   3   9  15  15 

$su

  0   1   2   3   4   5 
140   6   6   3   2   1 

$rbc

abnormal   normal 
      18      140 

$pc

abnormal   normal 
      29      129 

$pcc

notpresent    present 
       144         14 

$rc

2.1 2.5 2.6 2.7 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9   4 4.1 4.2 4.3 
  2   1   1   1   2   1   1   3   1   5   2   1   3   2   5   1   3   1   3 
4.5 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6.0 6.1 6.2 6.3 
 12   3   7   9   9   7   4   8   7   5   6   6   4   4   5   3   3   5   3 
6.4 6.5 8.0 
  5   3   1 

$htn

 no yes 
124  34 

$dm

 no yes 
130  28 

$cad

 no yes 
147  11 

$appet

good poor 
 139   19 

$pe

 no yes 
138  20 

$ane

 no yes 
142  16 

#Function to convert factor variables into dummy variables
creat_dummy_var_data <- function(dataset){
  dummy_variables <- dummyVars(~., data=dataset, fullRank=T)
  dummy_var_data <- data.frame( predict(dummy_variables, newdata=dataset) )
  return(dummy_var_data)
}

#Function to Split data into Training and Test
create_training_test <- function(features_dataset,outcome_data,training_test_ratio){
  training_index <- createDataPartition(outcome_data,p=training_test_ratio,list=F)
  
  training_set <- droplevels(features_dataset[training_index,])
  test_set <- droplevels(features_dataset[-training_index,])
  
  outcome_training_set <- factor(outcome_data[training_index])
  outcome_test_set <- factor(outcome_data[-training_index])
  
  return(list(training_features=training_set, test_features=test_set, training_outcome=outcome_training_set, test_outcome=outcome_test_set))
}

remove_nonvaring_collinear_features <- function(training_data,test_data,corr_theshold=0.75){
  # remove zero covaritates (features with NO VARIABILITY)
  #nearZeroVar(training_data,saveMetrics = T)
  near_zero_covariates <- colnames(training_data)[nearZeroVar(training_data)]
  
  if(length(near_zero_covariates)>0)
  {
    # find column indices of the near_zero_covariates
    nzc_indices_training <- sapply(near_zero_covariates,function(i) {grep( paste("^",i,"$",sep=""),colnames(training_data))})
    training_data_nzc <- training_data[,-nzc_indices_training]
    
    nzc_indices_test <- sapply(near_zero_covariates,function(i) {grep( paste("^",i,"$",sep=""),colnames(test_data))})  
    test_data_nzc <- test_data[,-nzc_indices_test]
  } else {
    training_data_nzc <- training_data
    test_data_nzc <- test_data
  }

  # CORRELATED features
  feature_correlation <- cor(training_data_nzc)
  # search through a correlation matrix and returns a vector of integers corresponding to columns to remove to reduce pair-wise correlations.
  high_correlation <- findCorrelation(feature_correlation,corr_theshold,verbose=F,names=T)

  if(length(high_correlation)>0)
  {
    correlated_indices_training <- sapply( high_correlation,function(i) {grep( paste("^",i,"$",sep=""),colnames(training_data_nzc))} )
    final_training_data <- training_data_nzc[,-correlated_indices_training]
    
    correlated_indices_test <- sapply( high_correlation,function(i) {grep( paste("^",i,"$",sep=""),colnames(test_data_nzc))} )
    final_test_data <- test_data_nzc[,-correlated_indices_test]
  }else{
    final_training_data <- training_data_nzc
    final_test_data <- test_data_nzc
  }
  
  return(list(processed_training_set=final_training_data, processed_test_set=final_test_data))
}
#Execution

#sapply(Chronic_Kidney_Disease2[1,],class)
# since many of the features are categorical, convert them into dummy varaibles except the outcome. 
outcome_column_id <- grep("class",colnames(Chronic_Kidney_Disease2))
dataset_dummy_variables <- creat_dummy_var_data(Chronic_Kidney_Disease2[,-outcome_column_id])

# split data
# IMPORTANT NOTE: the class of column used for creating DataPartion is very #important. Same variable can give different training_index depending on whether #it is numeric or factor.


set.seed(123)
split_data <- create_training_test(dataset_dummy_variables,Chronic_Kidney_Disease2$class,0.6)
lapply(split_data,head)

$training_features

$test_features

$training_outcome
[1] ckd ckd ckd ckd ckd ckd
Levels: ckd notckd

$test_outcome
[1] ckd ckd ckd ckd ckd ckd
Levels: ckd notckd

# data preprocessing
processed_data <- remove_nonvaring_collinear_features(split_data$training_features,split_data$test_features,0.75)
#lapply(processed_data,dim)

final_training_set <- processed_data$processed_training_set
final_test_set <- processed_data$processed_test_set
#dim(final_training_set); dim(final_test_set)

training_output <- split_data$training_outcome
test_output <- split_data$test_outcome
#length(training_output); length(test_output)

#Exploratory Graphs
# PCA
pc <- prcomp(final_training_set,center=T,scale=T)
plot(pc,type="l",lab=c(10,10,12))

#pc$rotation[order(-abs(pc$rotation[,"PC1"])),]
par(xpd=TRUE)
par(mfrow=c(2,2))
for(i in seq_along(colnames(final_training_set)))
{
  plot(training_output,final_training_set[,i],main=colnames(final_training_set)[i],col=2:3)
}

par(mfrow=c(1,1))

#Model Building
# k-fold cross validation
train_control <- trainControl(method="cv", number=5, savePredictions = T,classProbs =  TRUE)

# svm - linear
set.seed(1)
svm_lm_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "svmLinear",preProcess = c("center", "scale","pca"))

install.packages("kernlab")

Error in install.packages : Updating loaded packages

# svm - rbf kernel
set.seed(1)
svm_rbf_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "svmRadial", tuneLegth=5, preProcess = c("center", "scale","pca"))

# Ridge Regression creates a linear regression model that is penalized with the L2-norm which is the sum of the squared coefficients.
set.seed(1)
ridge_regression_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "glmnet",family = "binomial",tuneGrid=expand.grid(alpha=0,lambda=0.001),preProcess = c("center", "scale","pca"))
install.packages("kernlab")

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# LASSO (Least Absolute Shrinkage and Selection Operator) creates a regression model that is penalized with the L1-norm which is the sum of the absolute coefficients. 
set.seed(1)
lasso_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "glmnet",family = "binomial",tuneGrid=expand.grid(alpha=1,lambda=0.001),preProcess = c("center", "scale","pca"))

# Elastic Net creates a regression model that is penalized with both the L1-norm and L2-norm. 
set.seed(1)
elastic_net_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "glmnet",family = "binomial",tuneGrid=expand.grid(alpha=0.5,lambda=0.001),preProcess = c("center", "scale","pca"))

# classification Trees
set.seed(1)
rpart_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "rpart",preProcess = c("center", "scale","pca"))
#plot(rpart_model)

# random forest
set.seed(1)
rf_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "rf",prox=T,preProcess = c("center", "scale"))

plot(rf_model)

# boosting with tres
set.seed(1)
gbm_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "gbm", verbose=F,preProcess = c("center", "scale","pca"))

#plot(gbm_model)

# linear discriminant analysis
set.seed(1)
lda_model <- train(y=training_output, x=final_training_set, trControl=train_control, method = "lda",preProcess = c("center", "scale","pca"))

# collect resamples
training_models <- list(SVM_LM=svm_lm_model,SVM_RBF=svm_rbf_model,RPART=rpart_model,GBM=gbm_model,RF=rf_model,LDA=lda_model,RIDGE=ridge_regression_model,LASSO=lasso_model,ELASTIC=elastic_net_model)
train_results <- resamples(training_models)
# summarize the distributions
summary(train_results)

Call:
summary.resamples(object = train_results)

Models: SVM_LM, SVM_RBF, RPART, GBM, RF, LDA, RIDGE, LASSO, ELASTIC 
Number of resamples: 5 

Accuracy 
             Min.   1st Qu. Median      Mean 3rd Qu. Max. NA's
SVM_LM  0.9473684 0.9500000      1 0.9794737       1    1    0
SVM_RBF 0.8000000 1.0000000      1 0.9600000       1    1    0
RPART   1.0000000 1.0000000      1 1.0000000       1    1    0
GBM     1.0000000 1.0000000      1 1.0000000       1    1    0
RF      1.0000000 1.0000000      1 1.0000000       1    1    0
LDA     0.9500000 1.0000000      1 0.9900000       1    1    0
RIDGE   0.9000000 0.9473684      1 0.9694737       1    1    0
LASSO   1.0000000 1.0000000      1 1.0000000       1    1    0
ELASTIC 0.9473684 1.0000000      1 0.9894737       1    1    0

Kappa 
             Min.   1st Qu. Median      Mean 3rd Qu. Max. NA's
SVM_LM  0.8549618 0.8750000      1 0.9459924       1    1    0
SVM_RBF 0.5652174 1.0000000      1 0.9130435       1    1    0
RPART   1.0000000 1.0000000      1 1.0000000       1    1    0
GBM     1.0000000 1.0000000      1 1.0000000       1    1    0
RF      1.0000000 1.0000000      1 1.0000000       1    1    0
LDA     0.8750000 1.0000000      1 0.9750000       1    1    0
RIDGE   0.7368421 0.8549618      1 0.9183608       1    1    0
LASSO   1.0000000 1.0000000      1 1.0000000       1    1    0
ELASTIC 0.8549618 1.0000000      1 0.9709924       1    1    0

# boxplots of results
bwplot(train_results)

# the above results suggest that svm-rf and random forest model performs best on the training data.

#EVALUATE MODEL ACCURACY ON TEST SET

#Ideally, you select model that performs best on training data and evaluate on test set. I am doing for all models just for illustration 
test_pred_svm_lm <- predict(svm_lm_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_svm_lm, reference=test_output)

test_pred_svm_rbf <- predict(svm_rbf_model, newdata=final_test_set)
confusionMatrix(data=test_pred_svm_rbf, reference=test_output)

Confusion Matrix and Statistics

          Reference
Prediction ckd notckd
    ckd     17      1
    notckd   0     45
                                          
               Accuracy : 0.9841          
                 95% CI : (0.9147, 0.9996)
    No Information Rate : 0.7302          
    P-Value [Acc > NIR] : 6.034e-08       
                                          
                  Kappa : 0.9605          
                                          
 Mcnemar's Test P-Value : 1               
                                          
            Sensitivity : 1.0000          
            Specificity : 0.9783          
         Pos Pred Value : 0.9444          
         Neg Pred Value : 1.0000          
             Prevalence : 0.2698          
         Detection Rate : 0.2698          
   Detection Prevalence : 0.2857          
      Balanced Accuracy : 0.9891          
                                          
       'Positive' Class : ckd             
                                          

test_pred_rpart <- predict(rpart_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_rpart, test_output)

test_pred_gbm <- predict(gbm_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_gbm, test_output)

test_pred_rf <- predict(rf_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_rf, test_output)

test_pred_lda <- predict(lda_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_lda, test_output)

test_pred_ridge <- predict(ridge_regression_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_ridge, test_output)

test_pred_lasso <- predict(lasso_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_lasso, test_output)

test_pred_elastic_net <- predict(elastic_net_model, newdata=final_test_set)
#confusionMatrix(data=test_pred_elastic_net, test_output)

balanced_accuracy <- function(trained_model, test_features=final_test_set, test_outcomes=test_output){
  test_model <- predict(trained_model,test_features)
  test_score <- confusionMatrix(data=test_model, test_outcomes)
  return(test_score$byClass[["Balanced Accuracy"]])
}

lapply(training_models, balanced_accuracy)

$SVM_LM
[1] 0.9411765

$SVM_RBF
[1] 0.9891304

$RPART
[1] 1

$GBM
[1] 1

$RF
[1] 1

$LDA
[1] 0.9411765

$RIDGE
[1] 0.8823529

$LASSO
[1] 0.9411765

$ELASTIC
[1] 0.9411765

#ROC CURVES

roc_curve <- function(test_predictions,colour=1,test_labels=test_output){
  pred <- prediction(as.numeric(test_predictions), as.numeric(test_labels) )
  perf <- performance(pred, measure = "tpr", x.measure = "fpr")
  plot(perf,col=colour)
}

roc_curve(test_pred_svm_rbf,colour=1)
par(new = TRUE)

roc_curve(test_pred_lda,colour=2)
par(new = TRUE)

roc_curve(test_pred_rf,colour=3)
par(new = TRUE)

roc_curve(test_pred_elastic_net,colour=4)
par(new = TRUE)

roc_curve(test_pred_gbm,colour=5)
par(new = FALSE)

legend("bottomright",c("svm radial kernel", "lda","random forest","elastic_net","graded boosting"), col = c(1:5),cex=0.8,lty=1)
title(main="ROC curves for test data")

plot(varImp(svm_rbf_model))

ckd_training_set <- which(training_output=="ckd")
nonckd_training_set <- which(training_output=="notckd")

imp_features_svm <- (c("rbc.c","bu","sod","bgr","age"))
sapply(imp_features_svm,function(i) {
  t.test(final_training_set[ckd_training_set,i],final_training_set[nonckd_training_set,i])
})

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