Abstract: Ｏｂｊｅｃｔｉｖｅ：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.
Ｍｅｔｈｏｄｏｌｏｇｙ：A total of 240 patients who received HD treatment at the Eastern Theater Command General Hospital in January 2016 were screened, and dialysis quality was assessed more than three times a year. The follow-up period ends in October 2022, and the endpoint is death from all causes. 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. Dialysis quality indicators include interdialytic weight gain、pre-dialysis systolic blood pressure、hemoglobin、albumin、total carbon dioxide、calcium、phosphorus、parathyroid hormone and spKt／V.A prediction model for survival or death of HD patients after 1 year was constructed based on a machine learning algorithm, and the optimal probability threshold of the model was obtained.
Ｒｅｓｕｌｔｓ：After 94 months of follow-up, 60 patients (25.0%) died. Six machine learning methods, KNN, RandomForest, ExtraTrees, XGBoost, AdaBoost and DecisionTree, 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 0.9 respectively, while confirming 0.65 as the optimal probability threshold for the model.
Ｃｏｎｃｌｕｓｉｏｎ：The machine learning model based on the indicator time-to-standard ratio has a good prediction effect on the prognosis of HD patients.

Key words: hemodialysis, dialysis quality, machine learning, prognosis