Prediction of cardiovascular disease risk based on major contributing features

Abstract

The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learning model, which is a CVD risk prediction model based on key contributing features. In this paper, the generalisation of the model was enhanced by adding retrospective data of 14,832 Chinese Shanxi CVD patients to the kaggle dataset. The XGBH risk prediction model proposed in this paper was validated to be highly accurate (AUC = 0.81) compared to the baseline risk score (AUC = 0.65), and the accuracy of the model for CVD risk prediction was improved with the inclusion of the conventional biometric BMI variable. To increase the clinical application of the model, a simpler diagnostic model was designed in this paper, which requires only three characteristics from the patient (age, value of systolic blood pressure and whether cholesterol is normal or not) to enable early intervention in the treatment of high-risk patients with a slight reduction in accuracy (AUC = 0.79). Ultimately, a CVD risk score model with few features and high accuracy will be established based on the main contributing features. Of course, further prospective studies, as well as studies with other populations, are needed to assess the actual clinical effectiveness of the XGBH risk prediction model.

Predictive Modeling in Medicine

“…Recently, Peng et al identified age, smoking status, and blood pressure as primary predictors of cardiovascular disease by developing a predictive model that utilized data from a sizable population-based study. This effective method recognized high-risk individuals [10]. The research underscores the importance of these factors in preventing and managing cardiovascular disease.…”

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

“…SVM classi es data by separating classes with a boundary [26,27], whereas XGBoost is a powerful tool for classi cation and regression [27]. XGBoost has been utilized to predict outcomes such as heart failure and cardiovascular events based on patient data and risk factors, demonstrating its e ciency and accuracy in this domain [28,29]. A literature review and research articles indicate that LightGBM, a distributed gradient boosting framework known for its speed and e ciency, has been increasingly applied in healthcare and cardiovascular research.…”

Multi-layered metabolic effects of trehalose on the liver proteome in apoE-knockout mice model of liver steatosis

“…Despite the clinical and research efforts over decades, cardiovascular diseases (CVD) remain a rapidly growing health problem in Western countries [1]. Atherosclerosis results in life-threatening occlusion of the blood vessels as a consequence of lipid-driven plaque buildup, characterized by endothelial activation, accumulation of oxidized low-density lipoproteins, and subsequent migration of monocytes and other inflammatory cells [2].…”