Decision Tree for Predicting the Mortality in Hemodialysis Patient with Diabetes

Abstract

Hemodialysis patients with diabetes face a significantly higher risk of mortality compared to those without diabetes. Accurate prediction of mortality in this patient population is crucial for guiding clinical decision-making, improving patient outcomes, and optimizing resource allocation. Hemodialysis is a procedure for cleaning the blood from the waste products of the body's metabolism. this is one of modality to treat end stage kidney disease. Diabetes mellitus is a significant contributor to the global burden of chronic kidney disease (CKD), and patients with diabetes undergoing hemodialysis are at a higher risk of mortality compared to those without diabetes. Identifying factors that influence mortality risk in this population can aid in clinical decision-making and improve patient outcomes. Dialysis is performed on patients with kidney failure, both acute kidney failure and chronic kidney failure. This study is aimed to predict the mortality risk of hemodialysis patients with diabetes. The Taiwanese hemodialysis center enrolled a total of 665 hemodialysis patients. The prediction is based on Decision Tree. Compared with K-Nearest Neighbor, linear discriminant, Logistic Regression, and Ensemble, Decission Tree performed better. As for related medical variables like parathyroid surgery, urea reduction ratio, etc., they play a much smaller role in mortality risk factors than diabetes and cardiovascular disease.

Diabetes

GS Cited Analysis