Use of Machine Learning Models to Predict Mortality in Dialysis Patients

Junmin Huang¹LU Chen¹Hongying Luo¹Junhao Song²Ziqian Bi²Keyu Chen²Chia Xin Liang²Ming Liu²Tianyang Wang²Benji Peng³Zihan Wei¹Zhiqing Huang¹Li Zhihang¹Xincheng Liu¹Hongjiu Zhou¹Weihuang Zhang¹Wen Wen¹Mian-Na Luo¹Shujun Wang¹Huafeng Liu¹Chunjie Tian¹Jibin Guan⁴Joe Yeong⁵Yongzhi Xu^1*Peng Wang^1*Junfeng Hao^1*

• ¹Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
• ²AI Agent Lab, Vokram Group, London, United Kingdom, London, United Kingdom
• ³AppCubic, Atlanta, Georgia, USA, Atlanta, United States
• ⁴University of Minnesota Masonic Cancer Center, Minneapolis, United States
• ⁵Singapore General Hospital Division of Pathology, Singapore, Singapore

Background Mortality among maintenance hemodialysis patients remains high, and traditional statistical models often fail to capture complex clinical relation-ships. This study aimed to systematically develop, compare, and validate 19 machine learning algorithms for predicting all-cause mortality in maintenance hemodialysis patients. Methods This retrospective study included data from 538 maintenance hemodialysis patients (2018.1–2023.12), with 70% used for training and 30% for testing. Each model underwent hyperparameter optimization based on three performance metrics (accuracy, F1-score, and ROC Area Under the Curve [AUC]) to evaluate the impact of different clinical priorities. Results Gradient boosting models demonstrated consistent superiority, with performance outcomes highly sensitive to the selected optimization target. XGBoost optimized for accuracy achieved an F1 score of 0.683 and a ROC AUC of 0.899. AdaBoost optimized for F1 score attained the highest ROC AUC of 0.903 and an F1 score of 0.682. AdaBoost also demonstrated robust performance across optimization strategies, suggesting its suitability for clinical implementation where balanced risk prediction is essential. Conclusion A systematic ML framework can yield tailored, high-performing models for mortality risk stratification in maintenance hemodialysis patients, with significant potential to enhance identification and management of high-risk individuals in clinical practice.