Predicting CKD progression using time-series clustering and light gradient boosting machines

Predicting CKD progression using time-series clustering and light gradient boosting machines

Abstract Predicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict...

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Bibliographic Details
Main Authors: Hirotaka Saito, Hiroki Yoshimura, Kenichi Tanaka, Hiroshi Kimura, Kimio Watanabe, Masaharu Tsubokura, Hiroki Ejiri, Tianchen Zhao, Akihiko Ozaki, Sakumi Kazama, Michio Shimabukuro, Koichi Asahi, Tsuyoshi Watanabe, Junichiro J. Kazama
Format: Article
Language:English
Published: Nature Portfolio 2024-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-52251-9

Internet

https://doi.org/10.1038/s41598-024-52251-9

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