Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.

ABSTRACT:

Background

Cardiovascular disease (CVD) is a leading public health issue worldwide. The key to preventing CVD is the early prediction and identification of CVD risk factors. The aim of this study is to construct and validate CVD prediction models using machine learning (ML).

Methods

This study utilized 11 features from 68,205 CVD respondents in the Kaggle dataset. Experiments were conducted using a feature decomposition-based deep learning model (FDDL) to predict CVD incidence in this dataset. The proposed model was compared with six other machine learning models. Moreover, the SHAP method was employed to interpret the model in this study.

Results

The FDDL model demonstrated superior predictive capability, achieving benchmark metrics of 75.52% accuracy, 78.14% precisi

SUBMITTER: Yu L 

PROVIDER: S-EPMC12570785 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature