Open AccessComparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening
Author(s) -
Xia Cao,
YanHui Lin,
Binfang Yang,
Ying Li,
Jiansong Zhou
Publication year - 2022
Publication title -
risk management and healthcare policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.828
H-Index - 22
ISSN - 1179-1594
DOI - 10.2147/rmhp.s346856
Subject(s) - renal function , machine learning , computer science , function (biology) , artificial intelligence , medicine , data mining , statistics , intensive care medicine , mathematics , biology , evolutionary biology
Using machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data.