MRMR-BAT-HS: A Clinical Decision Support System for Cancer Diagnosis
A novel clinical support system using a hybrid multi-stage biomarker gene identification method MRMR-BAT-HS is proposed for biomarker gene selection from the microarray datasets. The microarray dataset consists of irrelevant, redundant, and noisy genes; from these few informative genes are needed to identify the cancer disease. Informative genes selection form a huge amount of genes is a challenging task. This is also known as the curse of dimensionality. Optimization algorithms are used for solving the gene selection problem. In this proposed method we have implemented both filter and wrapper method biomarker gene selection. In the filter stage, we have used MRMR (minimum redundancy and maximum relevance) to select the subset of featured genes. In the wrapper approach, we have combined two featured metaheuristic approaches (BAT-HS) with Support Vector Machine. This approach is applied with various microarray datasets to test accuracy performance using leave one out cross-validation method (LOOCV) method. Performance evaluation of the proposed one is compared with various gene selection methods. It suggests that the outcome of the proposed method is impressive than others. The relevancy of selected genes functions is investigated to check the classification performance superiority.