Multi-Rate Hybrid Control Framework for Coronary Heart Disease Risk Management

Sabahi, Farnaz

doi:10.22111/ieco.2026.54544.1742

Document Type : Research Articles

Author

Farnaz Sabahi

Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.

https://doi.org/10.22111/ieco.2026.54544.1742

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

Main Subjects

Control

References

[1] S. Vosbergen et al., "Using personas to tailor educational messages to the preferences of coronary heart disease patients," Journal of Biomedical Informatics, vol. 53, pp. 100–112, 2015, doi: https://doi.org/10.1016/j.jbi.2014.09.004.

[2] A. H. Association. 2026 Heart Disease & Stroke Statistics Update Fact Sheet Global Burden of Disease

[3] B. S. Rao, K. N. Rao, and S. P. Setty, "An approach for heart disease detection by enhancing training phase of neural network using hybrid algorithm," in 2014 IEEE International Advance Computing Conference (IACC), 21–22 Feb. 2014 2014, pp. 1211–1220, doi: 10.1109/IAdCC.2014.6779500.

[4] A. Lahsasna, R. N. Ainon, R. Zainuddin, and A. Bulgiba, "Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis," Journal of Medical Systems, vol. 36, no. 5, pp. 3293–3306, 2012/10/01 2012, doi: 10.1007/s10916-012-9821-7.

[5] J. Kim, J. Lee, and Y. Lee, " Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree.," Healthcare Informatics Research, vol. 21, no. 3, pp. 167–174, 2015.

[6] F. Soleimannouri, S. Khorashadizadeh, M. Farshad, and N. Mehrshad, "Adaptive Fuzzy Control of Blood Glucose Level in Patients with Type 1 Diabetes in Presence of Input Saturation," International Journal of Industrial Electronics Control and Optimization, vol. 8, no. 2, pp. 117–127, 2025, doi: 10.22111/ieco.2024.49122.1585.

[7] F. Sabahi and M. Akbarzadeh Tootoonchi, "Comparative Evaluation of Risk Factors in Coronary Heart Disease Based on Fuzzy Probability-Validity Modeling," (in Fa), Journal of Advances in Medical and Biomedical Research, vol. 22, no. 91, pp. 73–83, 2014. [Online]. Available: https://www.magiran.com/paper/1253445.

[8] V. Khatibi and G. A. Montazer, "A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment," Expert Systems with Applications, vol. 37, no. 12, pp. 8536–8542, 2010/12/01/ 2010, doi: http://dx.doi.org/10.1016/j.eswa.2010.05.022.

[9] S. Muthukaruppan and M. J. Er, "A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease," Expert Systems with Applications, vol. 39, no. 14, pp. 11657–11665, 2012/10/15/ 2012, doi: http://dx.doi.org/10.1016/j.eswa.2012.04.036.

[10] H. Turabieh, "A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease " American Journal of Operations Research, vol. 6, pp. 136–146 2016.

[11] F. Sabahi, "Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment," Journal of Biomedical Informatics, vol. 83, pp. 204–216, 2018/07/01/ 2018, doi: https://doi.org/10.1016/j.jbi.2018.03.016.

[12] W. Wiharto, H. Kusnanto, and H. Herianto, "Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm," Healthcare Informatics Research, vol. 22, no. 1, pp. 30–38, 2016.

[13] S. Demir, H. Selvitopi, and Z. Selvitopi, "An early and accurate diagnosis and detection of the coronary heart disease using deep learning and machine learning algorithms," Journal of Big Data, vol. 12, no. 1, p. 228, 2025/09/29 2025, doi: 10.1186/s40537-025-01283-7.

[14] A. Z. Baratpur, H. Vahdat-Nejad, E. Arslan, J. Hassannataj Joloudari, and S. Gaftandzhieva, "Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection," (in eng), Front Netw Physiol, vol. 5, p. 1658470, 2025, doi:10.3389/fnetp.2025.1658470.

[15] M. U. Rehman et al., "Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment," (in eng), Sci Rep, vol. 15, no. 1, p. 13361, Apr 17 2025, doi: 10.1038/s41598-025-96437-1.

[16] S. Sastry and M. Bodson, Adaptive Control: Stability, Convergence, and Robustness. Prentice-Hall, 1996.

[17] L. Cronbach, "Coefficient alpha and the internal structure of tests," (in English), Psychometrika, vol. 16, no. 3, pp. 297–334, 1951/09/01 1951, doi: 10.1007/bf02310555.

[18] S. Shariatnia, M. Ziaratban, A. Rajabi, A. Salehi, K. Abdi Zarrini, and M. Vakili, "Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study," (in eng), BMC Med Inform Decis Mak, vol. 22, no. 1, p. 85, Mar 29 2022, doi: 10.1186/s12911-022-01823-8.

[19] P. S. Asih, Y. Azhar, G. W. Wicaksono, and D. R. Akbi, "Interpretable Machine Learning Model For Heart Disease Prediction," Procedia Computer Science, vol. 227, pp. 439–445, 2023/01/01/ 2023, doi: https://doi.org/10.1016/j.procs.2023.10.544.

Multi-Rate Hybrid Control Framework for Coronary Heart Disease Risk Management

References

References

Volume 9, Issue 4December 2026Pages 481-490

Volume 9, Issue 4
December 2026
Pages 481-490