Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

A Model for Predicting and Identifying Influential Factors in Type 1 Diabetes Control Based on Metaheuristic Algorithms

Document Type : Research Paper

Authors
Farshad jafarieh
Mohammadreza Sanaei
Department of Information Technology Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Abstract
This study proposes a multi-objective optimization model for predicting and managing Type 1 Diabetes by integrating clinical, behavioral, and lifestyle factors into a mathematical framework. Utilizing NSGA-II and MOPSO algorithms, the model simultaneously minimizes blood glucose deviation, insulin cost, and the complexity of input features. Synthetic patient data, modeled on clinical norms, were used to simulate diverse patient scenarios. The results demonstrate that NSGA-II provides superior convergence, diversity, and predictive accuracy compared to MOPSO, although MOPSO offers faster execution. Feature importance analysis revealed carbohydrate intake, stress, and physical activity as the most influential factors in glycemic control. The proposed model strikes a balance between predictive performance and interpretability, serving as a robust decision-support tool in personalized diabetes management. Its flexibility and transparency make it suitable for integration into clinical practice and future intelligent health systems.
Keywords
Type 1 Diabetes
Multi-objective Optimization
NSGA-II
MOPSO
Predictive Modeling
Subjects
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  • Receive Date 26 August 2024
  • Revise Date 23 November 2024
  • Accept Date 24 December 2024