Machine learning improves risk stratification of coronary heart disease and stroke
Bangwei Chen1,2,3, Lei Ruan4, Liuqiao Yang2,3,5
1School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
Insights
Machine learning models accurately predict coronary heart disease (CHD) and cerebral ischemic stroke (CIS) risk in the Asian population. Key risk factors include age, pulse wave velocity, hypertension, and LDL-C, aiding cardiovascular disease management.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- Cardiovascular diseases (CVD), including coronary heart disease (CHD) and cerebral ischemic stroke (CIS), pose a significant global health burden.
- Accurate risk stratification for CVD is crucial but lacks specific models for Asian populations.
- Machine learning (ML) offers potential for enhanced disease prediction and risk assessment in CVD.
Purpose of the Study:
- To develop and validate a clinically applicable ML-based risk stratification model for CHD and CIS.
- To identify common and unique risk factors for CHD and CIS in the studied population.
Main Methods:
- A case-control study utilizing 8,624 electronic medical records from 2008-2019.
- Development of two ensemble ML models (CHD and CIS) using random down-sampling and random forest methods.
- Model interpretation via Shapley Additive exPlanations (SHAP) to identify key predictors.
Main Results:
- The ML models demonstrated high performance with AUCs of 0.895-0.905 for CHD and 0.884-0.889 for CIS.
- Identified four common risk factors for CHD and CIS: age, brachial-ankle pulse wave velocity, hypertension, and low-density lipoprotein cholesterol (LDL-C).
- Carcinoembryonic antigen (CEA) was identified as an independent predictor for CHD.
Conclusions:
- The developed ensemble ML models offer clinically applicable risk stratification for CHD and CIS.
- Model interpretation provides valuable insights into shared and distinct risk indicators for these conditions.
- Findings can enhance the understanding and management of CVD risk factors in the Asian population.
Background:
Coronary heart disease (CHD) and cerebral ischemic stroke (CIS) are two major types of cardiovascular disease (CVD) that are increasingly exerting pressure on the healthcare system worldwide. Machine learning holds great promise for improving the accuracy of disease prediction and risk stratification in CVD. However, there is currently no clinically applicable risk stratification model for the Asian population. This study developed a machine learning-based CHD and CIS model to address this issue.
Methods:
A case-control study was conducted based on 8,624 electronic medical records from 2008 to 2019 at the Tongji Hospital in Wuhan, China. Two machine learning methods (the random down-sampling method and the random forest method) were integrated into 2 ensemble models (the CHD model and the CIS model). The trained models were then interpreted using Shapley Additive exPlanations (SHAP).
Results:
The CHD and CIS models achieved good performance with the areas under the receiver operating characteristic curve (AUC) of 0.895 and 0.884 in random testing, and 0.905 and 0.889 in sequential testing, respectively. We identified 4 common factors between CHD and CIS: age, brachial-ankle pulse wave velocity, hypertension, and low-density lipoprotein cholesterol (LDL-C). Moreover, carcinoembryonic antigen (CEA) was identified as an independent indicator for CHD.
Conclusions:
Our ensemble models can provide risk stratification for CHD and CIS with clinically applicable performance. By interpreting the trained models, we provided insights into the common and unique indicators in CHD and CIS. These findings may contribute to a better understanding and management of risk factors associated with CVD.
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