An Improved Squirrel Search Optimization for Medical Data Classification Model

Abstract

At present times, computer aided diagnosis (CAD) models become familiar in the healthcare sector. Medical diagnosis is identified to be subjective and it is based on the available data as well as physician’s experience. Machine learning (ML) techniques are commonly employed to design effective CAD models due to its stronger ability of identifying the complicated relationship in the medical data. This paper aims to develop a novel data classification model using squirrel search algorithm (SSA) with Mode Ranking method called MRISSA for healthcare diagnosis. Experiments were done on heart disease datasets available in Kaggle to evaluate the performance of the suggested technique. The outcomes show how effective the hybrid (MRISSA+ XGBoost) strategy is improved.