Key result
XGBoost model predicts cardiovascular disease risk with high accuracy, achieving a 0.95 ROC-AUC.
Why the study?
Predictive models for cardiovascular disease risk assessment must be explainable and reliable for clinical decision-making, in addition to being highly accurate.
Absolute Event Rate: 0.95% vs 0.92%
An explainable machine learning and deep learning framework using synthetic ECG and clinical data demonstrates high predictive performance and interpretability for cardiovascular risk assessment.
Promising ECG-based ML performance on synthetic data warrants caution before clinical use; leaves open real-world validation and outcome impact.
Predictive models used for the assessment of cardiovascular disease (CVD) risk must not only be highly accurate but also explainable and reliable for clinical decision-making. In this paper, we present a methodological framework for explainable machine learning and deep learning approaches to CVD risk prediction that uses clinically derived electrocardiogram (ECG) features and conventional CVD risk factors. A large synthetic dataset containing 30,000 patient cases was created according to a clinically validated distribution to provide reproducible and privacy-preserving benchmarking. A series of interpretability tests were performed on multiple models of machine learning and deep learning categories within a unified experimental setup. Out of the tested models, XGBoost and BiLSTM provided the highest discrimination capabilities in the machine learning and deep learning categories, respectively. Explaining the predictions via the SHapley Additive explanations (SHAP) approach, calibration of probability estimations, uncertainty quantification, and ablation studies were applied to assess the explainability, reliability, and robustness of the models. We show that the presented framework allows for the production of consistent and interpretable predictions along with the evaluation of the models’ explainability and predictive reliability. Instead of focusing on the single-feature importance, this paper presents a reproducible methodological framework for the explainable modeling of CVD risk.
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Suliman Aladhadh (2026) studied Cardiovascular disease risk (n=30,000). XGBoost machine learning model vs. Logistic Regression was evaluated on Area under the receiver operating characteristic curve (ROC-AUC) for cardiovascular disease risk prediction. The XGBoost model achieved the highest discrimination for cardiovascular disease risk prediction with a ROC-AUC of 0.95 and an accuracy of 0.89.
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