Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.17.2.3
A Novel Approach for Diabetes Mellitus Detection Using a Modified Binary Multi- Neighbourhood Artificial Bee Colony Algorithm with Mahalanobis-Based Feature Selection (MBMNABC-Ma) and an Optimized Decision Forest Framework
Gaurav Pradhan
orcid.org/0000-0002-8417-9786
; Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University (SMU), Majitar, India
*
Gopal Thapa
orcid.org/0000-0002-1709-4619
; Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University (SMU), Majitar, India
Ratika Pradhan
; Department of Computer Applications, Sikkim University, Gangtok, India
Bidita Khandelwal
; Department of General Medicine, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University (SMU), Tadong, India
* Dopisni autor.
Sažetak
Diabetes is a critical global health issue caused by high blood sugar (hyperglycemia), leading to complications like cardiovascular disease, blindness, neuropathy, and kidney failure. Machine learning (ML) algorithms improve both the accuracy and efficiency of medical diagnoses. This study applies a Modified Binary Multi-Neighbourhood Artificial Bee Colony with Mahalanobis- based (MBMNABC-Ma) for a feature selection algorithm, combined with diverse ML models for diabetes identification. Compared to the conventional Binary Multi-Neighbourhood Artificial Bee Colony (BMNABC), MBMNABC-Ma improves classification accuracy and reduces computational complexity. Five diabetes datasets were analyzed using a 70-30% holdout cross-validation. The MBMNABC- Ma model, trained on Optimal Decision Forest (ODF) with Random Forest Ensemble (RFE), demonstrated high effectiveness. It achieved 97.23% accuracy on the Merged Datasets (comprising 130 US and PIMA datasets), 97.93% on the Iranian Ministry of Health Dataset, 96.05% on the Questionnaire Dataset, 98.39% on the Hospital of Sylhet Dataset, and 80.98% on the PIMA Dataset, with high specificity and sensitivity scores across all cases.
Ključne riječi
Machine Learning; Feature Selection; Biomedical Data Analysis; Ensemble Learning; Diabetes Detection; Data Mining;
Hrčak ID:
344006
URI
Datum izdavanja:
2.2.2026.
Posjeta: 0 *