Enhancing cardiovascular risk prediction in Asian populations: A machine learning approach integrated with digital health platforms.

Authors

Sazzli Kasim, Putri Nur Fatin Amir Rudin, Xue Ning Kiew, Nurulain Ibrahim, Nafiza Mat Nasir, Lim Bing Feng, Hanis Hamidi, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff, Suraya Abdul-Razak, Kazuaki Negishi, Sorayya Malek

Published in

International journal of cardiology. Cardiovascular risk and prevention. Volume 27. Pages 200509. Epub Sep 09, 2025.

Abstract

This study aimed to develop and validate a machine learning (ML)-based model for cardiovascular disease (CVD) risk prediction in a Malaysian cohort representative of the Southeast Asian population.
Data from the Responding to Increasing Cardiovascular Disease Prevalence (REDISCOVER) Study, including 10,044 participants, were analyzed, with 4,299 cases retained after exclusions. The dataset was split into training (70 %) and validation (30 %) subsets. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) models were developed using feature selection techniques such as recursive feature elimination (RFE) and sequential backward elimination (SBE). Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, calibration, and Net Reclassification Index (NRI).
Among the models evaluated, the SVM model with SBE-selected features performed best, achieving an AUC of 0.800. This was higher than the Framingham Risk Score (FRS; AUC = 0.693), Revised Pooled Cohort Equations (RPCE; AUC = 0.744), and WHO CVD charts (AUC = 0.741). NRI analysis showed significant improvements compared to FRS and RPCE (17.29 % and 14.23 %, respectively; p < 0.00001). Calibration analyses indicated initial overprediction by ML models, which was mitigated by Platt scaling.
ML-based models incorporating regionally relevant variables demonstrated improved discrimination and reclassification compared with conventional risk scores in this Malaysian cohort. Further external validation is needed to establish their utility across broader Southeast Asian populations.

PMID:
41036185
Bibliographic data and abstract were imported from PubMed on 02 Oct 2025.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.