1. Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data
- Author
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Yuki Yoshizaki, Kiminori Kato, Kazuya Fujihara, Hirohito Sone, and Kohei Akazawa
- Subjects
chronic kidney disease ,health examination ,estimated glomerular filtration rate ,proteinuria ,recurrent neural network ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundChronic 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.MethodsData 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) 0.8 for predicting the onset of CKD in 1 year when the outcome was eGFR 0.9. With LR and a neural network, the specificities were 0.749 and 0.739 and AUROCs were 0.889 and 0.890, respectively, for predicting onset within 5 years. The AUROCs of most models were approximately 0.65 when the outcome was eGFR
- Published
- 2024
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