Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data

Chronic kidney disease (CKD) is characterized by a decreased glomerular filtration rate or renal injury (especially proteinuria) for at least 3 months. The early detection and treatment of CKD, a major global public health concern, before the onset of symptoms is important. This study aimed to devel...

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Bibliographic Details
Published in:Frontiers in public health 2024-11, Vol.12, p.1495054
Main Authors: Yoshizaki, Yuki, Kato, Kiminori, Fujihara, Kazuya, Sone, Hirohito, Akazawa, Kohei
Format: Article
Language:English
Subjects:
Adult
Aged
chronic kidney disease
estimated glomerular filtration rate
Female
Glomerular Filtration Rate
health examination
Humans
Incidence
Logistic Models
Machine Learning
Male
Middle Aged
proteinuria
Proteinuria - diagnosis
Proteinuria - epidemiology
recurrent neural network
Renal Insufficiency, Chronic - epidemiology
Risk Assessment - methods
Risk Factors
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Summary:Chronic kidney disease (CKD) is characterized by a decreased glomerular filtration rate or renal injury (especially proteinuria) for at least 3 months. The early detection and treatment of CKD, a major global public health concern, before the onset of symptoms is important. This study aimed to develop machine learning models to predict the risk of developing CKD within 1 and 5 years using health examination data. Data were collected from patients who underwent annual health examinations between 2017 and 2022. Among the 30,273 participants included in the study, 1,372 had CKD. Demographic characteristics, body mass index, blood pressure, blood and urine test results, and questionnaire responses were used to predict the risk of CKD development at 1 and 5 years. This study examined three outcomes: incident estimated glomerular filtration rate (eGFR)
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2024.1495054