Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.

ABSTRACT:

Objective

This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD.

Methods

This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP).

Results

The experimental results show that the AutoGluo

SUBMITTER: Wang J 

PROVIDER: S-EPMC11246996 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature