An Online Machine Learning Algorithm-Based Prognostic Predictive Model for Maintenance Hemodialysis Patients

Background: High mortality rates in maintenance hemodialysis (MHD) patients necessitate precise predictive tools. Existing models lack accuracy and ease of clinical access. This study focuses on constructing a precise and user-friendly machine learning-based mortality risk predictive model for MHD patients. Methods: A total of 601 MHD patients from Shantou Central Hospital were enrolled in this study. Clinical and laboratory data were meticulously gathered and assessed. Patients were divided randomly into Training (70%) and Test cohort (30%). Six types of machine learning algorithms based predictive models were constructed for prognostic prediction. The predictive accuracy of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC). Additionally, an online predictive model application was developed for practical clinical application. Results: The Training and Test cohort exhibited comparable demographic and clinical traits. Age, BMI, HGB, CH, AST, and serum albumin levels emerged as significant independent predictors of prognosis. The Extreme Gradient Boosting (XGBoost) based model predictive performance measures included with AUROC 0.831 and AUPRC 0.310 in the Test cohort. The XGBoost based model was selected as the definitive predictive tool and was made accessible via a web application. Conclusion: We successfully developed a machine learning-driven predictive model to predict the risk factors of MHD patients, which was then integrated into a user-friendly web application. This predictive tool could help to identify the high-risk factors of MHD patients in clinical practices.