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

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

SUBMITTER: Kasim S 

PROVIDER: S-EPMC12173414 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature