Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine

TL;DR

This paper presents a hybrid algorithm combining Modified Particle Swarm Optimization and Least Squares Support Vector Machine for classifying type II Diabetes Mellitus, achieving high accuracy on a standard dataset.

Contribution

It introduces a novel hybrid approach that optimizes LS-SVM parameters using Modified-PSO, improving classification robustness and accuracy.

Findings

Achieved 97.83% average classification accuracy.

Outperformed other classifiers on the same dataset.

Demonstrated robustness of the hybrid algorithm.

Abstract

Diabetes Mellitus is a major health problem all over the world. Many classification algorithms have been applied for its diagnoses and treatment. In this paper, a hybrid algorithm of Modified-Particle Swarm Optimization and Least Squares- Support Vector Machine is proposed for the classification of type II DM patients. LS-SVM algorithm is used for classification by finding optimal hyper-plane which separates various classes. Since LS-SVM is so sensitive to the changes of its parameter values, Modified-PSO algorithm is used as an optimization technique for LS-SVM parameters. This will Guarantee the robustness of the hybrid algorithm by searching for the optimal values for LS-SVM parameters. The pro-posed Algorithm is implemented and evaluated using Pima Indians Diabetes Data set from UCI repository of machine learning databases. It is also compared with different classifier algorithms which were applied on the same database. The experimental results showed the superiority of the proposed algorithm which could achieve an average classification accuracy of 97.833%.