Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.

ABSTRACT:

Background

Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression.

Methods

We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the e

SUBMITTER: Lei N 

PROVIDER: S-EPMC9341041 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature