All-Cause Mortality Prediction in Subjects with Diabetes Mellitus Using a Machine Learning Model and Shapley Values. - Mirea, Oana; Oghli, Mostafa Ghelich; Neagoe, Oana; Berceanu, Mihaela; Țieranu, Eugen; Moraru, Liviu; Raicea, Victor; Donoiu, Ionuț (Diabetology, 2025)
Background/Objectives: Diabetes mellitus (DM) is a prevalent disease with an increased risk of complications. Identifying risk factors for mortality in these patients is crucial, as early recognition can facilitate prompt therapeutic intervention. Machine learning (ML) models have proved to be valuable tools in different scenarios of healthcare decision making. We aimed to develop and test an ML model to predict all-cause mortality in a large cohort of subjects with DM. Methods: We included 1969 consecutive patients with DM type 1 (T1DM, n = 255) and type 2 (T2DM, n = 1714). eXtreme Gradient Boosting (XGBoost) was used for the prediction of all-cause mortality in this cohort and the Shapley additive explanation (SHAP) was used to assess the importance of each feature of the classifier. The missing values were imputed using the Missforest methodology. Results: The all-cause mortality rate was 21% during 5.5 ± 1.1 years of follow-up. The ML model achieved 90% sensitivity and 87% specificity with an AUC of 0.88 and an accuracy of 88% for predicting all-cause mortality. The SHAP analysis identified a lower glomerular filtration rate (eGFR), duration of insulin therapy, and a lower level of hemoglobin as the first three factors that contribute to the higher mortality rate. Conclusions: ML models can become valuable tools in clinical practice due to their unique ability to simultaneously assess the cumulative influence of multiple parameters and discover high-order interactions. The application of such models in clinical practice could improve the early identification of subjects at risk for complications and mortality and prompt early therapeutical interventions.