Table 3 Algorithms used in different studies

Decision Tree (DT)

[8, 10, 45,46,47, 54, 54]

Logistic Regression (LR)

[37, 45, 51, 52, 54, 55]

Generalized Linear Model (GLM)

[8, 47]

K Means Cluster (KMC)

[48]

Support Vector Machine (SVM)

[8, 42, 45, 47, 50, 51, 53,54,55]

J48

[44, 46]

Naïve Bayes (NB)

[8, 46, 53, 54]

PART

[44]

Multivariate Analysis (MA)

[34, 35, 38,39,40,41, 43, 52]

C5.0 Decision Tree (DT)

[9]

Random Forest (RF)

[10, 36, 42, 45, 46, 50, 54, 55]

XgBoost (XB)

[45, 55]

Balanced Random Forest (BRF), AdaBoost Ensemble (AE), Gradient Boosting (GB)

[36]

K Nearest Neighbors (KNN)

[10, 50]

C4.5 Decision Tree (DT)

[33, 42]

Clustering PAM

[53]

Univariate Analysis (UA)

[38,39,40,41, 52]

Random Tree (RT), Decision Table

[46]

Neural Network (NN)

[9, 10, 54]

Recurrent Neural Network

[49]

Back Propagation Neural Network (BPNN)

[45]

Classification And Regression Trees (CART)

[42]

Multilayer Perceptron Neural Networks (MLP)

[42]