Table 1 Identification of optimal machine learning models from each algorithm

Decision Tree (Gain Ratio)

0.963

0.014

False

0.98

0.926

0.952

True

0.952

0.988

0.957

Decision Tree (Gini Index)

0.956

0.008

False

0.961

0.925

0.942

True

0.952

0.976

0.964

Neural Network—Multilayer perceptron (Min–Max normalization)

0.904

0.015

False

0.784

0.867

0.86

True

0.976

0.952

0.927

Neural Network—Multilayer perceptron (Z-score normalization)

0.919

0.015

False

0.941

0.857

0.897

True

0.905

0.962

0.933

Random Forest (Min–Max normalization, Gini Index)

0.911

0.021

False

0.824

0.933

0.875

True

0.964

0.9

0.931

Random Forest (Z-score normalization, Gini Index)

0.948

0.013

False

0.941

0.923

0.932

True

0.952

0.964

0.958

Random Forest (Min–Max normalization, Information Gain Ratio)

0.881

0.008

False

0.745

0.927

0.826

True

0.964

0.862

0.91

Random Forest (Z-score normalization, Information Gain Ratio)

0963

0.008

False

0.961

0.942

0.951

True

0.964

0.976

0.97

Naïve Bayes (Z-score normalization)

0.622

0.0

False

0

–

–

True

1

0.622

0.767

Naïve Bayes (Min–max normalization)

0.378

0.005

False

1

0.378

0.548

True

0

–

–

Logistic Regression (Min–max normalization)

0.889

0.025

False

0.745

0.95

0.835

True

0.976

0.863

0.916

Logistic Regression (Z-score normalization)

0.956

0.019

False

0.961

0.925

0.942

True

0.952

0.976

0.964