Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations

Our aim is to build and evaluate models to screen for clinically significant nephrolithiasis in overweight and obesity populations using machine learning (ML) methodologies and simple health checkup clinical and urine parameters easily obtained in clinics. We developed ML models to screen for clinically significant nephrolithiasis (kidney stone > 2 mm) in overweight and obese populations (body mass index, BMI ≥ 25 kg/m2) using gender, age, BMI, gout, diabetes mellitus, estimated glomerular filtration rate, bacteriuria, urine pH, urine red blood cell counts, and urine specific gravity. The data were collected from hospitals in Kaohsiung, Taiwan between 2012 and 2021. Of the 2928 subjects we enrolled, 1148 (39.21