A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis

PurposeTo develop an assistant tool based on machine learning for early frailty screening in patients receiving maintenance hemodialysis.MethodsThis is a single-center retrospective study. 141 participants' basic information, scale results and laboratory findings were collected and the FRAIL scale was used to assess frailty. Then participants were divided into the frailty group (n = 84) and control group (n = 57). After feature selection, data split and oversampling, ten commonly used binary machine learning methods were performed and a voting classifier was developed.ResultsThe grade results of Clinical Frailty Scale, age, serum magnesium, lactate dehydrogenase, comorbidity and fast blood glucose were considered to be the best feature set for early frailty screening. After abandoning models with overfitting or poor performance, the voting classifier based on Support Vector Machine, Adaptive Boosting and Naive Bayes achieved a good screening performance (sensitivity: 68.24% +/- 8.40%, specificity:72.50% +/- 11.81%, F1 score: 72.55% +/- 4.65%, AUC:78.38% +/- 6.94%).ConclusionA simple and efficient early frailty screening assistant tool for patients receiving maintenance hemodialysis based on machine learning was developed. It can provide assistance on frailty, especially pre-frailty screening and decision-making tasks.

Automated summary

This study developed an assistant tool based on machine learning for early frailty screening in patients receiving maintenance hemodialysis. After feature selection, data split and oversampling, a voting classifier based on Support Vector Machine, Adaptive Boosting and Naive Bayes achieved a good screening performance.