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

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 analysismethod and factor analysismodel 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 systemof 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 thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.