Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data.

ABSTRACT: The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model's hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants wi

SUBMITTER: Kigka VI 

PROVIDER: S-EPMC9221964 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature