A Proposed Model for Diabetes Mellitus Classification using Coyote Optimization Algorithm and Least Squares Support Vector Machine

Abstract

ABSTRACT

One of the most dangerous health diseases affecting the world's population is Diabetes Mellitus (DM), and its diagnosis is the key to its treatment. Several methods have been implemented to diagnose diabetes patients. In this work, a hybrid model which combines of Coyote Optimization Algorithm (COA) and Least Squares Support Vector Machine  is proposed to classify of  patients.  classifier is applied for classification process but it's very sensitive when its parameter values are changed. To overcome this problem,  algorithm is implemented to optimize parameters of the  algorithm. This is the goal of the proposed model called the . The proposed model is implemented and evaluated using Pima Indians Diabetes Dataset (PIDD). Also it's compared with several classification algorithms that were implemented on the same PIDD. The experimental results demonstrated the effectiveness of the proposed model and its superiority over other algorithms, as it could accomplish an average classification accuracy of 98.811%.