# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 4 Discussion  
Artificial intelligence (AI) has expanded across many industries, including healthcare. Advanced algorithms have emerged to address intricate medical scenarios where traditional approaches have encountered limitations [39]. Notably, ML, a subfield of AI, has been integrated into medical registries to enhance the prediction of clinical events, demonstrating superior accuracy compared to human judgment [40]. This underscores the growing potential of AI-driven solutions to revolutionize healthcare by augmenting decision-making and improving patient outcomes. In this study, we reviewed the predictive performance and characteristics of the ML models investigated in the literature for predicting mortality risk in HD patients at various follow-up points. Logistic regression showed a predicted true positive mortality rate of 8.23% compared to an actual mortality rate of 10.53%, whereas XGBoost and Random Forest showed predicted true positive mortality rates of 9.93% and 8.94%, respectively, compared to an actual rate of 13.73%. Logistic Regression appears to demonstrate potential for accurately predicting mortality. Nevertheless, it is essential to compare this model with other models trained on the same dataset to ascertain its superiority. Regarding model sensitivity and specificity, the highest sensitivity was reported for the linear discriminant analysis model at one-year follow-up despite having a very low specificity, while the lowest sensitivity was reported for the Random Forest model at one year follow-up, despite its high specificity. The sensitivity of the Random Forest model was improved at two-year follow-up. The highest AUC was reported for the Random Forest model at three-year follow-up (0.89) and the lowest AUC was reported for the Decision Tree model at one-year follow-up (0.59), despite the fact that meta-analysis at one-year follow-up showed no significant sub-group difference between Random Forest and Logistic Regression models. [PERSON] et al., 2021 demonstrated that Logistic Regression achieved the best predictive performance over Decision Tree, Neural Network, and Support Vector Machine [37]. Conversely, other reports contradicted these findings [10; 29; 32]. For instance, [PERSON] et al., 2020 reported that mortality prediction models developed using Random Forest demonstrated promising levels of accuracy, with reported area under the curve (AUC) values ranging from 0.68 to 0.73. Furthermore, these models exhibited superior performance as compared to traditional Logistic Regression models at two-year follow-up [10]. Moreover, [PERSON] et al., 2023 and [PERSON] et al., 2023 reported that Decision Tree, Random Forest, Gradient Boosting, and XGBoost models showed better predictive capabilities in comparison to Logistic Regression [29; 32]. The predictive performance of different models depends on various clinical, laboratory, and demographic factors. In particular, Logistic Regression predictions depend primarily on age, type of vascular access, and albumin levels [36; 41; 42], whereas Random Forest models detected variables as levels of albumin, urea, and hemoglobin, as well as age and BMI as the major predictors of mortality [10]. Therefore, the predictive ability of the models differs according to the variables supplied. In addition, a small sample size may reduce the predictive ability of the model; thus, studies that trained their models with a large dataset may reflect greater accuracy. The primary strength of this study is that it is the first to review the ML predictors of mortality in HD patients by comparing the AUC, sensitivity, and specificity of the models. In addition, our findings underscore the potential of ML models as valuable tools in the clinical management of hemodialysis patients, a population facing exceedingly high mortality risk.