# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results
### Meta-Analysis  
The meta-analysis was conducted to evaluate the predictive performance of ML models, represented by the Area Under the Curve (AUC), for Random Forest and Logistic Regression models at a one-year follow-up. Four studies assessed the Random Forest model, demonstrating significant results, with a pooled mean difference (MD) of 0.78 (95% CI: 0.74-0.82, \(p<\) 0.00001) (Figure 6). Similarly, the Logistic Regression model was evaluated in four studies, yielding a pooled MD of 0.77 (95% CI: 0.66-0.87, \(p<\) 0.00001). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Follow Up**} & \multirow{2}{*}{**ML Model**} & \multirow{2}{*}{**AUC**} & \multicolumn{2}{c}{95\% CI} \\ \cline{5-5}  & & & & **LL** & **UL** \\ \hline \multirow{8}{*}{Sheng 2020 [30]} & \multirow{8}{*}{1 year} & Adaptive Boosting & 0.83 & 0.8 & 0.84 \\ \cline{3-5}  & & Gradient Boosting & 0.84 & 0.82 & 0.85 \\ \cline{3-5}  & & K-Nearest Neighbor & 0.82 & 0.81 & 0.86 \\ \cline{3-5}  & & Light Gradient Boosting & 0.85 & 0.8 & 0.85 \\ \cline{3-5}  & & Logistic Regression & 0.73 & 0.73 & 0.86 \\ \cline{3-5}  & & Random Forest & 0.82 & 0.8 & 0.85 \\ \cline{3-5}  & & XGBoost & 0.85 & 0.81 & 0.86 \\ \hline \multirow{4}{*}{Abilgic 2019 [31]} & 1 month & & 0.719 & 0.699 & 0.738 \\ \cline{2-5}  & 3 months & & 0.745 & 0.735 & 0.755 \\ \cline{2-5}  & 6 months & & 0.750 & 0.743 & 0.758 \\ \cline{2-5}  & 1 year & & 0.749 & 0.742 & 0.755 \\ \hline \multirow{8}{*}{Lee 2023 [32]} & \multirow{8}{*}{3 years} & Logistic Regression & 0.734 & NR & NR \\ \cline{3-5}  & & Decision Tree & 0.59 & NR & NR \\ \cline{3-5}  & 1 year & Random Forest & 0.806 & NR & NR \\ \cline{3-5}  & & Gradient Boosting & 0.793 & NR & NR \\ \cline{3-5}  & & XGBoost & 0.734 & NR & NR \\ \cline{3-5}  & & Logistic Regression & 0.756 & NR & NR \\ \cline{3-5}  & & Decision Tree & 0.66 & NR & NR \\ \cline{3-5}  & 3 years & Random Forest & 0.763 & NR & NR \\ \cline{3-5}  & & Gradient Boosting & 0.773 & NR & NR \\ \cline{3-5}  & & XGBoost & 0.788 & NR & NR \\ \hline [PERSON] 2008 [33] & 1 year & Logistic Regression & 0.78 & NR & NR \\ \hline \multirow{4}{*}{[PERSON] 2012 [34]} & 7-12 months & Logistic Regression & 0.698 & 0.679 & 0.717 \\ \cline{2-5}  & 13-18 months & Logistic Regression & 0.717 & 0.696 & 0.737 \\ \cline{2-5}  & 19-24 months & Logistic Regression & 0.67 & 0.646 & 0.694 \\ \hline \multirow{8}{*}{[PERSON] 2021 [36]} & 3 months & & 0.8 & 0.797 & 0.802 \\ \cline{2-5}  & 6 months & Logistic Regression & 0.77 & 0.768 & 0.772 \\ \cline{1-1} \cline{2-5}  & 1 year & & 0.86 & 0.8577 & 0.8623 \\ \cline{1-1} \cline{2-5}  & 3 months & & 0.77 & 0.7667 & 0.7733 \\ \cline{1-1} \cline{2-5}  & 6 months & Support Vector Machine & 0.75 & 0.7484 & 0.7516 \\ \cline{1-1} \cline{2-5}  & 1 year & & 0.8 & 0.7982 & 0.8018 \\ \cline{1-1} \cline{2-5}  & 3 months & & 0.84 & 0.8365 & 0.8435 \\ \cline{1-1} \cline{2-5}  & 6 months & Random Forest & 0.81 & 0.8084 & 0.8116 \\ \cline{1-1} \cline{2-5}  & 1 year & & 0.81 & 0.8034 & 0.8166 \\ \hline \multirow{4}{*}{[PERSON] 2020 [38]} & 3 years & & 0.89 & NR & NR \\ \cline{1-1} \cline{2-5}  & 5 years & Random Forest & 0.82 & NR & NR \\ \cline{1-1} \cline{2-5}  & 8 years & & 0.77 & NR & NR \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
Clinical Significance of the Present Study  
This study provides valuable insights into the application of machine learning (ML) models for predicting mortality in hemodialysis (HD) patients, a population with high morbidity and mortality rates. By systematically reviewing and analyzing the predictive performance of various ML models, this study highlights their potential to supplement traditional risk prediction tools, particularly in identifying high-risk patients. Accurate early mortality prediction in HD patients is crucial for several reasons, including optimizing resource allocation, tailoring individualized treatment strategies, and potentially improving patient outcomes.