Optimizing Diabetes Prediction: The Role of Feature Selection Through Grey Wolf Algorithm in Sustainable Healthcare, Arshiya Begum, Sreenivasa Reddy
Arshiya Begum
Research Scholar，Department of Computer Science and Engineering
Acharya Nagarjuna University
India
ORCID：0000-0002-7820-5976
E.Sreenivasa Reddy
Professor
Department of Computer Science and Engineering，VIT-AP University
India
ORCID：0000-0001-7347-2680
Type of Paper: Research
Purpose: The study addresses the diabetes mellitus (DiabM) epidemic by developing predictive Machine Learning (ML) models to identify the disease early and prevent its complications in millions of individuals.
Design/Methodology/Approach: The study uses data processing techniques, including feature selection techniques using Grey Wolf Optimization (GWO) to identify relevant features. Different ML models are applied to the selected features to predict the accuracy. The Support Vector Machine (SVM) predicts the highest accuracy compared with all the other models.
Findings: GWO with ML model enhances predictive accuracy, achieving 90% accuracy on the PIMA dataset.
Original/Value of the paper: This study offers insights into GWO for predicting diabetes at the early stage, which benefits healthcare providers and researchers while improving patient outcomes and healthcare efficiency.
Research Limitations/Implications: The research can be extended by applying more machine learning and deep learning models with the live dataset for proposing the new model.
Practical Implications: :The proposed model predicts diabetes, resulting in better patient outcomes and healthcare professionals managing diabetes.
Keywords: Diabetes Mellitus (DiabM), feature selection, Prediction Model, Grey Wolf Optimization (GWO), Machine Learning Models (ML), Health care Sustainability, Deep Learning Models (DL).
Citation:Begum, A.*, and Reddy, E. S. (2026). Optimizing Diabetes Prediction: The Role of Feature Selection Through Grey Wolf Algorithm in Sustainable Healthcare. World Journal of Entrepreneurship, Management and Sustainable Development, Vol. 22, Nos 1-2, pp. xx-xx.