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

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...

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Bibliographic Details
Main Authors: Nuo Lei, Xianlong Zhang, Mengting Wei, Beini Lao, Xueyi Xu, Min Zhang, Huifen Chen, Yanmin Xu, Bingqing Xia, Dingjun Zhang, Chendi Dong, Lizhe Fu, Fang Tang, Yifan Wu
Format: Article
Language:English
Published: BMC 2022-08-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Artificial intelligence
Machine learning algorithm
Prediction models
Chronic kidney disease
CKD progression
Immunoglobulin A nephropathy
Online Access:https://doi.org/10.1186/s12911-022-01951-1

Internet

https://doi.org/10.1186/s12911-022-01951-1

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