Automatic Detection of Diabetes Diagnosis using Feature Weighted Support Vector Machines based on Mutual Information and Modified Cuckoo Search

TL;DR

This paper introduces a novel automatic diabetes diagnosis method using a feature-weighted SVM optimized with a modified cuckoo search, achieving high accuracy and faster classification on UCI data.

Contribution

It proposes a new MI-MCS-FWSVM approach combining feature selection, weighting, and parameter optimization for improved diabetes diagnosis.

Findings

Achieved 93.58% accuracy on UCI dataset.

Outperformed previous methods in accuracy and speed.

Effectively integrated PCA, mutual information, and modified cuckoo search.

Abstract

Diabetes is a major health problem in both developing and developed countries and its incidence is rising dramatically. In this study, we investigate a novel automatic approach to diagnose Diabetes disease based on Feature Weighted Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The proposed model consists of three stages: Firstly, PCA is applied to select an optimal subset of features out of set of all the features. Secondly, Mutual Information is employed to construct the FWSVM by weighting different features based on their degree of importance. Finally, since parameter selection plays a vital role in classification accuracy of SVMs, MCS is applied to select the best parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracy on UCI dataset. The experimental results demonstrate that our method outperforms the previous methods by not only giving more accurate results but also significantly speeding up the classification procedure.