Machine-Learning Algorithm of Two Continuous Assessment Methods of Dialysis Quality Indicators-Based Prediction Scheme for Assessing Mortality Risk in Patients on Maintenance Hemodialysis

Background: Use machine learning method to analyze the impact of two continuous assessment methods of dialysis quality indicators on the prognosis of maintenance hemodialysis (HD) patients Methods: A total of 240 patients who received HD treatment were screened, and dialysis quality was assessed more than three times a year. The indicator time-to-standard ratio and indicator fluctuation value were used as the evaluation methods for the continuous achievement of nine dialysis quality indicators.A prediction model for survival or death of HD patients after 1 year was constructed based on a machine learning algorithm.Shapley additive explanation (SHAP) values were used to measure the marginal contribution of each feature to the models. Results: Six machine learning methods are used to build prediction models based on the indicator time-to-standard ratio and the indicator fluctuation value. The ExtraTrees model based on the indicator time-to-standard ratio has the best prediction effect, with its accuracy, precision, recall, F1 score and area under the receiver operating curve reaching 0.92, 0.86, 0.96, 0.91 and 0.93 respectively, while confirming 0.65 as the optimal probability threshold for the model. Visualization of the TreeSHAP interpretation results of the prediction model helps physicians understand the global prediction mechanism of the model and explain the importance of a patient's characteristic parameters in influencing the outcome of the patient. Conclusion: The machine learning model based on the indicator time-to-standard ratio has a good prediction effect on the prognosis of HD patients.