Building and validating a machine learning model to predict coronary heart disease risk based on non-invasive
Bo Wu1, Kang Huang1, Xin Hong1
1Department of Cardiovascular Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Insights
A new machine learning model accurately predicts coronary heart disease (CHD) risk using clinical data. This tool aids in early screening and prevention of CHD, a leading cause of death.
Area of Science:
- Cardiology
- Machine Learning
- Public Health
Background:
- Coronary heart disease (CHD) is a major global cause of mortality.
- Early identification and intervention are critical for managing CHD.
- This study focuses on developing a machine learning (ML) model for CHD risk prediction.
Purpose of the Study:
- To develop and evaluate a predictive model for coronary heart disease (CHD) risk.
- To identify key clinical features contributing to CHD risk.
- To create a user-friendly tool for early CHD screening.
Main Methods:
- Utilized the Behavioral Risk Factor Surveillance System (BRFSS) dataset for model development and internal validation.
- Employed eight machine learning algorithms, including Light Gradient Boosting Machine (LightGBM).
- Validated the optimal model externally using the National Health and Nutrition Examination Survey (NHANES) dataset and SHAP analysis for feature importance.
Main Results:
- The LightGBM model achieved an AUC of 0.825 in internal validation and 0.851 in external validation.
- Key predictors identified include age, sex, hypertension, and dyslipidemia.
- A web-based calculator was developed for CHD risk prediction based on the LightGBM model.
Conclusions:
- The LightGBM-based CHD risk prediction model demonstrates high accuracy.
- This model shows significant potential for early screening and prevention of coronary heart disease.
- The developed tool can assist healthcare professionals in identifying high-risk individuals.
Background:
Coronary heart disease (CHD) remains a leading global cause of death. Early identification of high-risk individuals and timely intervention are crucial. This study developed and evaluated a predictive CHD risk model using machine learning (ML) techniques.
Methods:
The Behavioral Risk Factor Surveillance System (BRFSS) data were randomly split into a training set and an internal validation set in a 7:3 ratio. Variable screening was performed using univariate and multivariate logistic regression analyses. Subsequently, predictive models were developed using eight machine learning algorithms. Model performance on the internal validation set was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy, and the optimal model was selected based on these metrics. The National Health and Nutrition Examination Survey (NHANES) dataset was used for external validation of the optimal model. Shapley Additive exPlanations (SHAP) analysis was employed to visualize the importance of features.
Results:
Eight machine learning models were developed based on 12 clinical features. Among these models, the Light Gradient Boosting Machine (LightGBM) demonstrated the best performance, with an internal-validation cohort AUC of 0.825 (95% CI 0.821-0.829), sensitivity of 0.800, and specificity of 0.700, significantly outperforming the other models. The external-validation cohort achieved an AUC of 0.851 (95% CI 0.835-0.867). SHAP analysis identified age, sex, hypertension, and dyslipidaemia as key risk factors. A web-based calculator was developed based on the LightGBM model to predict CHD.
Conclusion:
LightGBM-based prediction model exhibits high accuracy in assessing CHD risk and holds promise as an effective tool for the early screening and prevention of CHD.
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