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Fig. 9. Performance results of soft voting classifier.

TABLE V.
SIGNIFICANCE OF THE PROPOSED METHOD COMPARED WITH THE RELATED STUDIES

Authors Year Balanced the dataset Practical Application Feature selection method Model Accuracy (%)
Hazra 2016 NO NO Pearson correlation coefficient SVM 98.51
[6] 2018 NO NO Univariate feature selection (chi2) VotingClassifier(ANN,LR) 98.50
2018 NO NO Binary teaching learning-based optimisation (FS-BTLBO) SVM 98.43
Khuriwal 2019 NO NO Recursive feature elimination (RFE) SVM 99
[7] 2019 NO NO Adaboost classifier 98.24
2021 NO NO Genetic algorithm Soft Voting 99.00
Allam 2021 NO NO Correlation analysis and principal component analysis SVM 97.45
[8] 2021 NO NO Hierarchical clustering random forest (HCRF) 97.05
2022 NO NO Principal component analysis ANN 98.3
Memon Variable importance measure method (VIM) Soft voting classifier
[9] 2022 YES YES (LR, DT, SVM) 99.3
Forward feature selection
Dhahri
[10] Pearson correlation and mutual information (PC-MI)

Ibrahim
[11]
HAQ
[12]

HUANG
[13]

Jumanto
[14]

Proposed Method

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