A Machine Learning Approach To Predicting Postoperative Complications In Cardiovascular Surgery

Abstract

Cardiovascular surgery, while often lifesaving, is associated with a significant risk of postoperative complications. Early and accurate prediction of such complications can improve patient outcomes and optimize resource utilization in clinical settings. In recent years, machine learning (ML) has emerged as a transformative tool in medical diagnostics and prognostics. This paper explores the application of machine learning algorithms to predict postoperative complications in cardiovascular surgery. Utilizing patient datasets that include preoperative, intraoperative, and postoperative variables, various ML models—such as logistic regression, decision trees, support vector machines (SVM), random forests, and neural networks—are trained and evaluated. The results indicate that ML models, particularly ensemble methods and deep learning approaches, can achieve high predictive accuracy, aiding clinicians in risk stratification and personalized treatment planning. Ethical considerations, challenges in model generalization, and integration into clinical workflows are also discussed. This study highlights the potential of ML to revolutionize predictive analytics in cardiovascular healthcare.