Development and validation of an artificial intelligence-based model for cardiovascular disease prediction using longitudinal data.

ABSTRACT:

Background

This research uses machine learning (ML) models to determine significant predictive factors for CVD events in Iran, a country with high mortality rates. The study evaluates the effectiveness of deep learning and mixed-effects logistic models in predicting 10-year CVD incidence using longitudinal TLGS data.

Methods

A total of 4,872 adults aged ≥ 30 years without a history of CVD at baseline (2006-2008) were followed until 2020. Following exclusions due to prevalent CVD or insufficient follow-up data, the final analytic sample comprised 1,942 men and 2,930 women. Baseline demographic, behavioral, and biochemical characteristics were utilized as input features. A clinical history examination was employed to identify and confirm incident CVD events, such as stroke and

SUBMITTER: Mahdavi M 

PROVIDER: S-EPMC12750590 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature