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Cited by 3Artificial Intelligence in HealthcareHealth Promotion and Cardiovascular PreventionMachine Learning in Healthcare

Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population

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Kasim, Sazzli, Amir Rudin, Putri Nur Fatin, Malek, Sorayya, Ibrahim, Nurulain, Kiew, Xue Ning, Nasir, Nafiza Mat, Ibrahim, Khairul Shafiq, Raja Shariff, Raja Ezman·2025·PLOS One
DOI 10.1371/journal.pone.0323949PMC PMC12173414
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Abstract

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.

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  • Recent studies from Malaysia and China suggest that regionally developed machine learning models can improve performance in local cohorts, but local validation, calibration assessment, and broader external testing remain essential before wider implementation.

    “At the same time, recent studies from Malaysia and China suggest that regionally developed machine learning models can improve performance in local cohorts, but these same studies also show why local validation, calibration assessment, and broader external testing remain essential before wider implementation (14, 15).”

    From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [14]

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Authors
Kasim, S., Amir Rudin, P. N. F., Malek, S., Ibrahim, N., Kiew, X. N., Nasir, N. M., Ibrahim, K. S., & Raja Shariff, R. E.
Affiliations
  • Universiti Teknologi MARA System (MY)
  • University of Malaya (MY)
Corresponding author
Malek, Sorayya
ORCID
Venue
PLOS One
Publisher
Public Library of Science (PLoS)
Published
2025-06-17
Volume
20
Issue
6
Pages
e0323949
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0323949
PMC
PMC12173414
OpenAlex
W4411363343
Cited by
3
References
3
Citations by year
2026: 3
FWCI
2.36
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Malaysian Ministry of Science, Technology, and Innovation
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
cardiovascular risk prediction, 10-year cardiovascular risk, machine learning risk models, Malaysian population, Framingham risk score, net reclassification improvement, SHAP feature importance, systolic blood pressure, waist circumference, risk stratification, online risk calculator
MeSH terms
Machine Learning, Heart Disease Risk Factors, Adult, Aged, Algorithms, Bayes Theorem, Cardiovascular Diseases, Female, Humans, Malaysia, Male, Middle Aged, Risk Factors, Risk Assessment, Support Vector Machine

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