Mortality risk prediction in emergency department patients: Modeling approaches and performance analysis with gradient boosting
Methods: This retrospective study analyzed data from 1,500 patients who visited a state hospital's emergency department between January 1 and August 31, 2024. Data were collected based on multidimensional features such as demographic information, vital signs, laboratory results, and clinical history. The Gradient Boosting algorithm was used to develop the model, and its performance was evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score.
Results: The Gradient Boosting model identified oxygen saturation, age, and heart rate as the most significant predictors of mortality. The CatBoost algorithm demonstrated the highest performance with an accuracy of 88.8% and an F1 score of 85%. The model was proven to be highly accurate in predicting mortality risk.
Conclusions: Gradient Boosting algorithms, particularly CatBoost, emerged as a reliable and effective tool for predicting mortality risk. This model can contribute to the development of clinical decision support systems in emergency department settings.
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Article Information
- Article Type Research Article
- Submitted February 21, 2026
- Published November 3, 2025
- Issue Vol. 11 No. 6 (2025)
- Section Research Article
