DiabML: AI-assisted diabetes diagnosis method with meta-heuristic-based feature selection

TL;DR

This paper introduces DiabML, an AI-assisted diabetes detection method that combines meta-heuristic feature selection with machine learning, demonstrating improved accuracy over existing approaches.

Contribution

The paper presents a novel hybrid approach using BWO for feature selection and SMOTE for imbalance handling in diabetes risk detection, leveraging AIoMT technology.

Findings

DiabML achieves 86.1% classification accuracy.

The method outperforms existing diabetes detection techniques.

Utilizes BWO and SMOTE for effective feature selection and data balancing.

Abstract

Diabetes is a chronic disorder identified by the high sugar level in the blood that can cause various different disorders such as kidney failure, heart attack, sightlessness, and stroke. Developments in the healthcare domain by facilitating the early detection of diabetes risk can help not only caregivers but also patients. AIoMT is a recent technology that integrates IoT and machine learning methods to give services for medical purposes, which is a powerful technology for the early detection of diabetes. In this paper, we take advantage of AIoMT and propose a hybrid diabetes risk detection method, DiabML, which uses the BWO algorithm and ML methods. BWO is utilized for feature selection and SMOTE for imbalance handling in the pre-processing procedure. The simulation results prove the superiority of the proposed DiabML method compared to the existing works. DiabML achieves 86.1\% classification accuracy by AdaBoost classifier outperforms the relevant existing methods.