Key result
Logistic regression model improves 10-year CVD risk prediction over standard risk scores to AUC 0.77.
Why the study?
Do machine learning models improve 10-year CVD risk prediction compared to traditional risk scores in a Malaysian population?
Cohort (n=5,688)
Do machine learning models improve 10-year CVD risk prediction compared to traditional risk scores in a Malaysian population?
Machine learning models, particularly Logistic Regression, outperformed traditional risk scores like FRS and RPCE in predicting 10-year CVD risk in a Malaysian cohort.
BACKGROUND: Cardiovascular disease (CVD) is a significant public health challenge in the Western Pacific region, including Malaysia. OBJECTIVE: This study aimed to develop and validate machine learning (ML) models to predict 10-year CVD risk in a Malaysian cohort, which could serve as a model for other Asian populations with similar genetic and environmental backgrounds. METHODS: Utilizing data from the REDISCOVER Registry (5,688 participants from 2007 to 2017), 30 clinically relevant features were selected, and several ML algorithms were trained: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Neural Network (NN) and Naive Bayes (NB). Ensemble model were also created using three commonly used meta learners, including RF, Generalized Linear Model (GLM), and Gradient Boosting Model (GBM). The dataset was split into a 70:30 train-test ratio, with 5-fold cross-validation to ensure robust performance. Model evaluation was primarily based on the Area Under the Curve (AUC), with additional metrics such as sensitivity, specificity, and the Net Reclassification Index (NRI) to compare the ML models against traditional risk scores like the Framingham Risk Score (FRS) and Revised Pooled Cohort Equations (RPCE). RESULTS: The LR model achieved the highest AUC of 0.77, outperforming the FRS (AUC = 0.72) and RPCE (AUC = 0.74). The ensemble model provided robust performance, though it did not significantly exceed the best individual model. SHAP (SHapley Additive exPlanations) analysis identified key predictors such as systolic blood pressure, weight and waist circumference. The study showed a significant NRI improvement of 13.15% compared to the FRS and 7.00% compared to the RPCE, highlighting the potential of ML approaches to enhance CVD risk prediction in Malaysia. The best-performing model was deployed on a web platform for real-time use, ensuring ongoing validation and clinical applicability. CONCLUSIONS: These findings underscore the effectiveness of ML models in improving CVD risk stratification and decision-making in Malaysia and beyond.
No takes yet. Share an insight, caveat, or question.
Kasim et al. (2025) conducted a cohort in Cardiovascular disease risk (n=5,688). Machine learning models (Logistic Regression) vs. Framingham Risk Score and Revised Pooled Cohort Equations was evaluated on 10-year CVD risk prediction (Area Under the Curve) (AUC 0.77). A Logistic Regression model improved 10-year cardiovascular risk prediction with an AUC of 0.77, outperforming the Framingham Risk Score (AUC 0.72) and Revised Pooled Cohort Equations (AUC 0.74).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: