# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 1 Introduction  
Hemodialysis (HD) remains a critical and life-sustaining treatment for patients with End-Stage Renal Disease (ESRD), a severe condition characterized by the irreversible loss of kidney function. Patients with ESRD represent approximately 89% of individuals requiring dialysis therapy, underscoring the high prevalence of this population within the dialysis setting [1,2]. Despite advances in treatment, hemodialysis patients experience significantly higher mortality rates than the general population, with cardiovascular diseases being a major contributing factor [3,4].  
The elevated risk of mortality in HD patients results from a complex interplay of factors. Advanced age, high estimated glomerular filtration rate (eGFR) at dialysis initiation, the use of vascular catheters, low serum albumin, and reduced hemoglobin levels have all been identified as critical predictors of poor outcomes [5, 6, 7, 8, 9]. Early risk stratification using these clinical indicators is essential for improving patient management and enhancing survival rates [10].  
Traditional risk prediction models, such as Cox proportional hazards regression and logistic regression, have been widely used to identify mortality risk factors in HD patients [8,11, 12, 13, 14, 15, 16, 17, 18, 19]. While they possess the flexibility to incorporate non-linear relationships and interaction effects through explicit inclusion of transformed variables (e.g., polynomial terms, splines) or interaction terms, identifying and specifying all relevant complex relationships in advance can be challenging. In practice, simpler model structures are often initially employed, which may not fully capture and predict unknown patterns inherent in complex biological data. This may potentially limit predictive performance compared to methods designed to automatically detect such patterns, especially when applied to large, heterogeneous patient populations [20].  
The rise of big data and advanced computational techniques has paved the way for machine learning (ML) approaches, which have shown promise in addressing the limitations of traditional statistical models. Unlike conventional methods, ML algorithms such as Random Forest and XGBoost can handle complex, non-linear relationships between variables and can adapt to evolving clinical datasets [21]. These models excel in identifying novel predictive features and have demonstrated superior accuracy in mortality risk prediction compared to traditional models [22, 23, 24, 25]. Additionally, ML models offer the capability to estimate individualized treatment effects, providing a more personalized approach to patient care.  
Since traditional risk prediction models often struggle with the complex, non-linear interactions present in hemodialysis patient data, ML may offer additional advantages in addressing these complexities. Therefore, the primary objectives of this systematic review and meta-analysis are to systematically identify and summarize studies that have developed and evaluated ML models for predicting all-cause mortality in hemodialysis patients. In addition, we aim to compare the reported predictive performance of various ML models, focusing on metrics such as Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, and specificity, with particular attention to commonly employed algorithms like Random Forest and Logistic Regression. These findings will help to evaluate the current state of ML application in this area and assess the potential clinical utility of these models for enhancing risk stratification and informing personalized patient management strategies in the hemodialysis setting.