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

Background: 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. Methods: 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) <60 mL/min/1.73 m Results: All models had predictive values, sensitivities, and specificities >0.8 for predicting the onset of CKD in 1 year when the outcome was eGFR <60 mL/min/1.73 m Conclusion: Machine learning models can predict the risk of CKD, and eGFR plays a crucial role in predicting the onset of CKD. However, it is difficult to predict the onset of proteinuria based solely on health examination data. Further studies must be conducted to predict the decline in eGFR and increase in urine protein levels.