Ensemble Machine Learning Models for Accurate Prediction of Acute Pancreatitis Risk

Acute pancreatitis is a potentially life-threatening condition characterized by inflammation of the pancreas, often requiring prompt diagnosis and management. Early and accurate prediction of acute pancreatitis risk can significantly improve clinical outcomes and resource allocation. This research proposes the development of ensemble machine learning models for accurate prediction of acute pancreatitis risk. By leveraging a comprehensive dataset of electronic health records and clinical data, we employ a systematic feature engineering process to extract relevant predictive features. Subsequently, we design and evaluate various ensemble techniques, including random forests, gradient boosting, and stacking models, to combine the strengths of multiple base learners. Extensive experiments are conducted to assess the performance of the proposed models using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Furthermore, we analyse the feature importance and interpretability of the models to gain insights into the critical risk factors contributing to acute pancreatitis. The results demonstrate the potential of the proposed ensemble approach in outperforming individual base learners and existing baseline methods for acute pancreatitis risk prediction, paving the way for improved clinical decision support and early intervention strategies.