Metaheuristic Model of Gene Selection for Deep Learning Early Prediction of Cancer Disease Using Gene Expression Data

Abstract

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of prediction algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, proposed a hybrid novel technique CSSMO, based feature selection for cancer prediction. First, combine the use of Spider Monkey Optimization (SMO) along with Cuckoo search (CS) algorithm viz. CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Next, these subsets of genes are classified using Deep Learning (DL) to identify different groups or classes related to a particular cancer disease. Six different datasets have utilized to analyze the performance of the proposed approach in terms of cancer sample classification and prediction with Recall, Precision, F1-Score, and confusion matrix. Proposed gene selection method with DL achieves much better prediction accuracy than other existing Deep Learning (DL) and Machine learning models with a large gene expression datasets.