Fig 1.
Method of journal papers’ selection.
Fig 2.
Number of released papers targeting IG and GA for cancer research.
Fig 3.
Number of released papers targeting mRMR and PSO for cancer research.
Table 1.
Comparison of the different parameters in previous work.
Table 2.
Gene expression datasets used in the investigations.
Fig 4.
Class label distribution for the colon cancer datasets.
Fig 5.
PCA for exploring the data structure of the datasets.
Fig 6.
The proposed framework model–HMLFSM.
Table 3.
The number of relevant genes (features) selected by the proposed model on each phase.
Fig 7.
Classification accuracy for the ML algorithms applied to Dataset 1.
Fig 8.
ML confusion matrix for the ML algorithms applied to Dataset 1.
Fig 9.
ROC area for the ML algorithms applied to Dataset 1.
Fig 10.
Classification accuracy for the ML algorithms applied to Dataset 2.
Fig 11.
Confusion matrix for the ML algorithms applied to Dataset 2.
Fig 12.
ROC area for the ML algorithms applied to Dataset 2.
Fig 13.
Classification accuracy for the ML algorithms applied to Dataset 3.
Fig 14.
ML confusion matrix for the ML algorithms applied to Dataset 3.
Fig 15.
ROC area for the ML algorithms applied on Dataset 3.
Fig 16.
Summary of ROC area for the ML algorithms applied to all datasets.
Table 4.
Phase 1 performance measures for Dataset 1.
Table 5.
Phase 1 performance measures for Dataset 2.
Table 6.
Phase 1 performance measures for Dataset 3.
Table 7.
Phase 2 performance measures for Dataset 1.
Table 8.
Phase 2 performance measures for Dataset 2.
Table 9.
Phase 2 performance measures for Dataset 3.
Table 10.
Comparison of the proposed model, with others in the literature.