Enhanced CAD patient length of stay prediction using the Graph Neuro Boost model

Coronary Artery Disease (CAD) remains one of the leading causes of mortality worldwide and accounts for a great number of prolonged hospital stays, which increase the cost of healthcare and its resource consumption. This research presents Graph Neuro Boost, a new method of predicting hospital LOS for CAD patients based on hybrid feature selection and classification, and characterized by high accuracy of prediction. Using the MIMIC-III dataset, the method involves complete data preparation, analysis, and preprocessing. A Graph Neuro Feature Selector was built by combining the autoencoder-based embedded method and the filter method using graph theory and Kruskal’s algorithm. The method efficiently identified 16 critical features in just 2.68 seconds, surpassing traditional hybrid feature selection approaches. Those features were then optimized using Bayesian optimization together with XGBoost, implementing a balanced Expected Improvement and Upper Confidence Bound technique to reduce log loss. Graph Neuro Boost resulted in an accuracy of 98.57%. In addition to superior accuracy, Graph Neuro Boost demonstrated outstanding performance relative to baseline models on multiple evaluation metrics. The SHAP analysis was also employed to interpret feature contributions, improving model transparency and supporting clinical decision-making, patient management, and efficient healthcare resource allocation.