Using random forest algorithm for glomerular and tubular injury diagnosis

ObjectivesChronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three...

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Veröffentlicht in:	Frontiers in medicine Jg. 9; S. 911737
Hauptverfasser:	Song, Wenzhu, Zhou, Xiaoshuang, Duan, Qi, Wang, Qian, Li, Yaheng, Li, Aizhong, Zhou, Wenjing, Sun, Lin, Qiu, Lixia, Li, Rongshan, Li, Yafeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Lausanne Frontiers Media SA 28.07.2022 Frontiers Media S.A
Schlagworte:	Alcohol Algorithms Artificial intelligence auxiliary diagnosis Blood pressure Body mass index Chronic illnesses Classification Creatinine Feature selection glomerular injury High density lipoprotein Kidney diseases Kidney transplants Lifestyles Machine learning Medicine Questionnaires random forest Regression analysis Sampling techniques tubular injury Variables
ISSN:	2296-858X, 2296-858X
Online-Zugang:	Volltext
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