Improved outcome prediction in acute pancreatitis with generated data and advanced machine learning algorithms
Murat Özdede1
, Ali Batur2, Alp Eren Aksoy2
1Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Türkiye
2Department of Emergency Medicine, Faculty of Medicine, Hacettepe University, Ankara, Türkiye
Keywords: Acute pancreatitis, machine learning, mortality, outcomes, prognosis, scoring methods
Abstract
OBJECTIVES: Traditional scoring systems have been widely used to predict acute pancreatitis (AP) severity but have limitations in predictive accuracy. This study investigates the use of machine learning (ML) algorithms to improve predictive accuracy in AP.
METHODS: A retrospective study was conducted using data from 101 AP patients in a tertiary hospital in Türkiye. Data were preprocessed, and synthetic data were generated with Gaussian noise addition and balanced with the ADASYN algorithm, resulting in 250 cases. Supervised ML models, including random forest (RF) and XGBoost (XGB), were trained, tested, and validated against traditional clinical scores (Ranson’s, modified Glasgow, and BISAP) using area under the curve (AUC), F1 score, and recall.
RESULTS: RF outperformed XGB with an AUC of 0.89, F1 score of 0.82, and recall of 0.82. BISAP showed balanced performance (AUC = 0.70, F1 = 0.44, and recall = 0.85), whereas the Glasgow criteria had the highest recall but lower precision (AUC = 0.70, F1 = 0.38, and recall = 0.95). Ranson’s admission criteria were the least effective (AUC = 0.53, F1 = 0.42, and recall = 0.39), probable because it lacked the 48th h features.
CONCLUSION: ML models, especially RF, significantly outperform traditional clinical scores in predicting adverse outcomes in AP, suggesting that integrating ML into clinical practice could improve prognostic assessments.
How to cite this article: Özdede M, Batur A, Aksoy AE. Improved outcome prediction in acute pancreatitis with generated data and advanced machine learning algorithms. Turk J Emerg Med 2025;25:32-40.
This study was conducted in accordance with international and national regulations, aligning with the Declaration of Helsinki, the Human Tissue Act 2004, and the Turkish Data Protection Law. Ethical approval for this study was obtained from Hacettepe University Ethics Committee in Türkiye on the date of 27th December 2022, with approval number of GO 22/1317.
The manuscript has been read and approved by all authors. Conceptualization: MO and AB, Data curation: MO, AB, and AEA, Formal analysis: MO, AB, and AEA, Methodology: MO and AB, Software: MO, Supervision: MO and AB, Validation: MO, Visualization: MO, AB, and AEA, Writing – Original Draft: MO, Writing – Review and Editing: AB and AEA.
None Declared.
None.