An interpretable LightGBM model for predicting coronary heart disease: Enhancing clinical decision-making with machine learning.

ABSTRACT:

Background

Coronary Heart Disease (CHD) is one of the major burdens of cardiovascular diseases worldwide. Traditional diagnostic methods, such as coronary angiography and electrocardiogram, face challenges including high costs, subjectivity, and high misdiagnosis rates. To address these issues, this study proposes a prediction framework for CHD based on the LightGBM algorithm, aiming to improve the accuracy and interpretability of CHD risk prediction.

Methods

This study utilized three publicly available datasets: BRFSS_2015, Framingham, and Z-Alizadeh Sani. The BRFSS_2015 dataset was used for model training, while the Framingham and Z-Alizadeh Sani datasets were employed for validation. Data preprocessing included cleaning, feature engineering, and handling missing values. T

SUBMITTER: Deng L 

PROVIDER: S-EPMC12431356 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature