Feature Mining and Classification of Microarray Data Using Modified ResNet-SVM Architecture

Microarray data have been one of the torch-bearing approaches when ushered for prediction and diagnosis of deadly diseases. But, much of its potential has been overshadowed by its structural orientation of low sample and high dimension which have provided the dilemma for researchers working in this field of study. In recent times, many approaches have been adopted to tackle this dilemma through feature extraction and thereafter classification that have stapled very concrete results. But, the methodologies lacked direct implementation and required the transformation of the data into a relevant intermediate matrix, before being mobilized for feature extraction and classification. In this paper, an effective hybrid alternative has been proposed which utilizes a modified single-dimensional (1D) Residual Network (ResNet) for lucid feature extraction directly from the gathered datasets and a Support Vector Machine (SVM) classifier for diseases classification. The proposed methodology is tested on three datasets (namely Colon cancer, Leukemia, and Lung cancer) and derived a staggering classification accuracy.