An effective feature selection method for type 2 diabetes mellitus detection using gene expression data

Abstract

Type 2 diabetes mellitus (T2DM) detection is a chronic disease, which is caused due to the insulin disorder. Moreover, the decreased secretion of insulin increased the blood glucose level, thereby the human body cannot respond with the high glucose level. The T2DM sufferers do not produce enough insulin, or it resists insulin. The symptoms of T2DM disease are increased hunger, thirst, fatigue, frequent urination and blurred vision, and in some cases, there are no symptoms. The commonly utilized treatments of T2DM are exercise, diet, insulin therapy and medication. In this paper, the Competitive Multi-Verse Rider Optimizer (CMVRO)-based hybrid deep learning scheme is devised for T2DM detection. The hybrid deep learning involves two classifiers, such as Rider based Neural Network (RideNN) and Deep Residual Network (DRN). Moreover, the comparative analysis of T2DM detection is done by comparing various feature selection approaches, such as Tanimoto similarity, Chi square (Chi-2), Fisher Score (FS), Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine recursive feature elimination (SVM-RFE) for T2DM detection. Amongst these, the tanimoto similarity feature selection approach attained the better performance with respect to the testing accuracy, sensitivity and specificity of 0.932, 0.932 and 0.914, correspondingly.