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Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cheng Wang1, Haoran Zhu2,*, Congjun Rao2,*

1 School of Mathematics and Statistics, Hubei University of Education, Wuhan, 430205, China
2 School of Science, Wuhan University of Technology, Wuhan, 430070, China

* Corresponding Authors: Haoran Zhu. Email: ; Congjun Rao. Email:

(This article belongs to this Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)

Computer Modeling in Engineering & Sciences 2024, 138(1), 691-718.


Cardiovascular disease (CVD) has gradually become one of the main causes of harm to the life and health of residents. Exploring the influencing factors and risk assessment methods of CVD has become a general trend. In this paper, a machine learning-based decision-making mechanism for risk assessment of CVD is designed. In this mechanism, the logistics regression analysis method and factor analysis model are used to select age, obesity degree, blood pressure, blood fat, blood sugar, smoking status, drinking status, and exercise status as the main pathogenic factors of CVD, and an index system of risk assessment for CVD is established. Then, a two-stage model combining K-means cluster analysis and random forest (RF) is proposed to evaluate and predict the risk of CVD, and the predicted results are compared with the methods of Bayesian discrimination, K-means cluster analysis and RF. The results show that the prediction effect of the proposed two-stage model is better than that of the compared methods. Moreover, several suggestions for the government, the medical industry and the public are provided based on the research results.


Cite This Article

Wang, C., Zhu, H., Rao, C. (2024). Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease. CMES-Computer Modeling in Engineering & Sciences, 138(1), 691–718.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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