Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients

While ML models demonstrated high predictive performance, their clinical applicability was limited due to complexity. The Bayesian-learning-based risk stratification model provided a simplified yet robust alternative, effectively predicting CRT risk and offering a clinically feasible tool for risk assessment in breast cancer patients with chemotherapy. Further validation in diverse cancer populations is warranted to refine its generalizability.