⚡ Quick Summary
This study systematically developed and validated 19 machine learning algorithms to predict all-cause mortality in maintenance hemodialysis patients. The Gradient Boosting models showed superior performance, with AdaBoost achieving the highest ROC AUC of 0.903.
🔍 Key Details
- 📊 Dataset: 538 maintenance hemodialysis patients (2018.1-2023.12)
- 🧩 Features used: Clinical data from hemodialysis patients
- ⚙️ Technology: 19 machine learning algorithms including XGBoost and AdaBoost
- 🏆 Performance: XGBoost: F1 score 0.683, ROC AUC 0.899; AdaBoost: F1 score 0.682, ROC AUC 0.903
🔑 Key Takeaways
- 📊 High mortality rates among hemodialysis patients necessitate improved predictive models.
- 💡 Machine learning offers a promising approach to enhance mortality risk stratification.
- 👩🔬 Study utilized a dataset of 538 patients for model training and validation.
- 🏆 AdaBoost demonstrated robust performance across various optimization strategies.
- 🌍 Clinical implications include better identification and management of high-risk patients.
- 🔍 Hyperparameter optimization was crucial for model performance evaluation.
- 🗓️ Study period spanned from January 2018 to December 2023.
- 🆔 Clinical Trial Number: ChiCTR2500103960.
📚 Background
Mortality rates among maintenance hemodialysis patients remain alarmingly high, often exceeding 20% annually. Traditional statistical models have struggled to capture the complex clinical relationships that contribute to patient outcomes. This gap highlights the need for innovative approaches, such as machine learning, to improve mortality predictions and ultimately enhance patient care.
🗒️ Study
The study was conducted as a retrospective analysis involving 538 maintenance hemodialysis patients over a five-year period. Researchers aimed to develop, compare, and validate various machine learning algorithms to predict mortality risk. The dataset was split into 70% for training and 30% for testing, ensuring robust model evaluation.
📈 Results
Among the tested models, Gradient Boosting algorithms consistently outperformed others. Specifically, XGBoost optimized for accuracy achieved an F1 score of 0.683 and a ROC AUC of 0.899. Meanwhile, AdaBoost, optimized for F1 score, reached the highest ROC AUC of 0.903 and an F1 score of 0.682. These results underscore the importance of model selection based on clinical priorities.
🌍 Impact and Implications
The findings from this study have significant implications for clinical practice. By employing a systematic machine learning framework, healthcare providers can achieve tailored, high-performing models for mortality risk stratification. This advancement has the potential to enhance the identification and management of high-risk individuals, ultimately improving patient outcomes in the hemodialysis population.
🔮 Conclusion
This study highlights the transformative potential of machine learning in predicting mortality among maintenance hemodialysis patients. The ability to develop high-performing models tailored to clinical needs can significantly improve risk stratification and patient management. As we continue to explore these technologies, the future of patient care in nephrology looks promising.
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Use of machine learning models to predict mortality in dialysis patients.
Abstract
BACKGROUND: Mortality among maintenance hemodialysis patients remains high, and traditional statistical models often fail to capture complex clinical relationships. 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.
CLINICAL TRIAL NUMBER REGISTRY: Chinese Clinica Trial Registry (ChiCTR), TRN:ChiCTR2500103960, Registration date: 9 June 2025.
Author: [‘Huang J’, ‘Chen L’, ‘Luo H’, ‘Song J’, ‘Bi Z’, ‘Chen K’, ‘Chia XL’, ‘Liu M’, ‘Wang T’, ‘Peng B’, ‘Wei Z’, ‘Huang Z’, ‘Li Z’, ‘Liu X’, ‘Zhou H’, ‘Zhang W’, ‘Wen W’, ‘Luo M’, ‘Wang S’, ‘Liu H’, ‘Tian C’, ‘Guan J’, ‘Yeong J’, ‘Xu Y’, ‘Wang P’, ‘Hao J’]
Journal: Front Public Health
Citation: Huang J, et al. Use of machine learning models to predict mortality in dialysis patients. Use of machine learning models to predict mortality in dialysis patients. 2025; 13:1683285. doi: 10.3389/fpubh.2025.1683285