Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery

Liang Shen, Yunpeng Jin, Axiang Pan, Kai Wang, Runze Ye, Yangkai Lin, Safraz Anwar, Weicong Xia, Min Zhou, Xiaogang Guo

Abstract: Highlights•We have adopted a series of widely used machine learning algorithms and model evaluation techniques to build clinical prediction models, and achieved better performance and clinical practicability than the classical RCRI model, which has taken the first step to explore the research in this field.•The prediction results based on the optimal machine learning model are interpretable, output the importance ranking and impact degree of the top 20 features of MACEs risk prediction, and are consistent with clinical interpretation, which is conducive to the application of the model in clinical practice.•We use Bayesian algorithm to automatically adjust the model hyperparameters, so that the selection of appropriate missing data imputation method, resampling technology and feature selection method can be combined with automatic hyperparameter tuning, and it is also confirmed that the appropriate resampling technology combined with feature selection can greatly improve the impact of data imbalance on model performance.•We put forward the stacking ensemble model, and use the exhaustion method to form 247 stacking models, evaluate the performance of each model in turn, and select the optimal stacking model. Compared with the optimal independent model XGBoost, the optimal stacking ensemble model showed slightly higher AUPRC performance and clinical utility, with higher sensitivity.

External IDs:dblp:journals/cmpb/ShenJPWYLAXZG25