Optimal Feature Subset Selection with Multi-Kernel Extreme Learning Machine for Medical Data Classification

Abstract

Medical data classification is treated as a crucial process in the domain of medical informatics. The recently developed machine learning (ML) algorithms are found useful for the medical diagnosis. This paper presents a new ML based medical data classification model using the whale optimization algorithm (WOA) based on feature selection with Multi-kernel extreme learning machine (MKELM) model, called WOA-MKELM. The proposed model could perform the medical data classification using two processes, namely the WOA based FS and the MKELM based classification. In the first stage, the WOA-FS algorithm is executed on the medical data to generate the feature reduced subset. In the second stage, the MKELM algorithm is applied to allocate the appropriate class labels for the medical data. The incorporation of WOA-FS model, prior to classification, helps to increase the detection rate significantly. The performance of the WOA-MKELM model has been tested using three medical datasets, namely hepatitis, UCI-Indian Liver Patient (UCI-ILD), and thyroid. The obtained experimental could values verify the effectiveness of the WOA-MKELM model with the maximum accuracy of 98.36%, 98.72% and 98.93%  for the applied hepatitis, UCI-ILD and thyroid dataset respectively.