Machine Learning Models for Predicting Mortality in Hemodialysis Patients - Lantmann, L. L.; Gauer, F. G.; Reinheimer, I. C.; Souza, D. C. de; Bernardes, M. M.; Poli-de-Figueiredo, C. E.; Musse, S. R. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2025.38297, 2025)
In Brazil, over 133,464 individuals with Chronic Kidney Disease (CKD) undergo hemodialysis, facing significant mortality risks. The mandatory biomarkers for monitoring these patients are specified by the 2014 Clinical Guideline of the Ministry of Health. Annually, more than thirty biomarkers are periodically evaluated; however, no critical evaluation of the predictive value of these biomarkers using machine learning (ML) has been conducted in Brazil to date. This paper aims to develop ML models to predict mortality outcomes in hemodialysis patients based on routine biomarkers. The goal is to investigate technologies that can assess the predictive effectiveness of clinical tests, ultimately improving patient quality of life and contributing to cost management within the Brazilian Unified Health System (SUS). This study utilizes data from hemodialysis patients in a retrospective cohort study conducted between 2012 and 2016 across 23 dialysis units in five Brazilian states. The features used in model development include biomarkers, patient profile variables, and clinical outcomes. Various ML approaches and algorithms are tested, including Decision Tree, Random Forest, Logistic Regression, XGBoost and TabPFN to identify and compare the most accurate predictive model. Among the tested models, TabPFN exhibited the best overall predictive performance, notably benefiting from balanced training data. Furthermore, the application of SHAP (SHapley Additive ExPlanations) provided clear and interpretable insights into the most influential biomarkers, which contributed to understanding the clinical plausibility of the results.