Hospital Admission Classification of Cardiac Patients Utilizing Metaheuristics-Optimized Two Tier Framework

Accurate evaluation of a cardiac patient’s risk at the point of hospital entry is critical for efficient triage and ensuring timely, suitable medical intervention. This study aims to forecast a range of clinical outcomes by leveraging admission data from a cardiac care unit, utilizing a refined and optimized machine learning approach. This research introduces a hybrid architecture that integrates convolutional neural networks (CNNs) with advanced machine learning classifiers, namely light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), further enhanced through metaheuristic optimization techniques to maximize their performance. The proposed two-tiered design organizes feature extraction and final decision modeling into a coherent pipeline tailored for multi-class hospital admission classification. A comprehensive evaluation using a real-world hospital admission dataset demonstrates the framework’s effectiveness on a real-world, publicly available hospital admission dataset, supporting its utility for multi-class cardiac outcome prediction. Three experiments were conducted using publicly available datasets, where the best-performing models achieved a peak classification accuracy of 99.79%. Furthermore, explainable AI techniques were employed to interpret model predictions, offering actionable insights that can guide future data acquisition and strengthen the accurate classification of cardiac patients.