xgbTuneGrid = expand.grid(
      nrounds = 1000,
      eta = c(0.01,0.1),
      max_depth =  c(2,6,10),
      gamma = 0,
      colsample_bytree=0.6,
      min_child_weight=1,
      subsample = 0.75
    )
    ctrl <- trainControl(method = "repeatedcv",number = 5,summaryFunction=twoClassSummary,classProbs=TRUE, allowParallel = TRUE)

    xgbtreeModel = train(readmitted ~., data = train, method = "xgbTree",tuneGrid = xgbTuneGrid,trainControl = ctrl)
    xgbtreeModel$resample
    xgbtreeModel$results

    xgb_prediction <- predict(xgbtreeModel, test)
    roc_xgboost =roc(test$readmitted, xgb.probs[,1])
    plot(roc_xg, col = flat_blue, main="ROC curve - tree" )
    confusionMatrix(xgb_prediction,test$readmitted)
    cor_xgboost = coords(roc_xg,"best", ret=c("accuracy", "sensitivity", "specificity"))