Fatih İKİZ, Ahmet AK
Eurasian Journal of Emergency Medicine - 2026;25(1):228-236
Aim: Accurate mortality prediction is fundamental for clinical resource allocation and personalized patient management across diverse medical conditions in patients admitted to emergency department. This study evaluates the predictive capacity of laboratory and clinical variables by conducting a direct comparison between conventional statistical models and advanced machine learning (ML) algorithms. Materials and Methods: Mortality-associated variables were first analyzed using conventional approaches, including basic comparative tests, logistic regression, and ROC analyses. Subsequently, eleven ML algorithms were deployed to benchmark their performance against these conventional methods. The dataset was partitioned into a 75% training set and a 25% testing set. Models were evaluated based on sensitivity (recall), area under the curve (AUC), and overall accuracy. Features of importance were defined as ranks assigned by the most robust model to identify key clinical predictors. Results: While conventional statistical methods achieved a maximum sensitivity of 81.2% (AUC=0.906), ML algorithms significantly outperformed conventional statistical methods. Among the eleven algorithms, BayesNet emerged as the superior model, with a sensitivity of 88.9% and an overall classification accuracy of 92.3%. The analysis demonstrates that ML techniques capture complex, non-linear interactions within clinical data that standard logistic regression may overlook. Conclusion: ML algorithms offer a substantial improvement in predictive performance over conventional statistical methods for mortality prediction in patients with complex viral diseases admitted to the emergency department. These findings suggest that integrating ML into clinical decision-support systems can provide more precise risk stratification than traditional prognostic tools.
