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

ABSTRACT:

Objectives

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.

Methods

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, speci

SUBMITTER: Kasim S 

PROVIDER: S-EPMC12482346 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature