A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease.

ABSTRACT:

Objective

To develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD).

Methods

16,645 cases of cardiopulmonary exercise testing (CPET) conducted in patients with CHD from January 2016 to September 2019 were retrospectively included. Clinical data before testing and data during exercise were collected and analyzed.

Results

Cardiovascular events occurred during 505 CPETs (3.0%). No death was reported. Predictive accuracy of the model was evaluated by area under the curve (AUC). AUCs for the SVM, logistic regression, GBDT and XGBoost were 0.686, 0.778, 0.784, and 0.794 respectively.

Conclusions

Machine learning methods (especially XGBoost) can effectively predict car

SUBMITTER: Shen T 

PROVIDER: S-EPMC9605581 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature