Document Type : Original Article

Author

• Farnaz Sabahi

Electrical and Computer Engineering Department, Urmia University

Abstract

This paper proposes a multi-rate hybrid control framework for modeling and managing Coronary Heart Disease (CHD) risk dynamics. Unlike conventional statistical and machine learning approaches that treat clinical variables as static predictors without dynamic interaction or stability guarantees, the proposed method formulates cardiovascular risk progression as a hybrid dynamical system with fast–slow time-scale decomposition and logical switching between physiological subsystems. A nonlinear switching controller incorporating decay synchronization is developed to regulate the evolution of risk states. Sufficient stability conditions are rigorously derived using a Lyapunov–Krasovskii functional, ensuring boundedness and convergence of the closed-loop system under disturbances and multi-rate sampling. The framework was evaluated using 152 clinical records collected from Iranian hospitals, with stratified training and testing procedures to ensure reliable evaluation. The proposed method achieved an accuracy of 82% and an F1-score of 0.83, demonstrating improved predictive consistency compared with conventional baseline models. The results indicate that the proposed hybrid control formulation provides both theoretical stability guarantees and practical predictive capability for dynamic health risk management.

Keywords

• Decision-Making
• Risk Assassment
• Coronary Heart Disease
• Multi-Rate Controller

Main Subjects

• Control