PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach

This study demonstrates the potential of machine learning models in assessing an individual's risk of developing osteoporosis, a condition that significantly impacts quality of life and imposes substantial healthcare costs. The superior performance of the LightGBM model suggests a promising tool for early detection and personalized prevention strategies. Importantly, identifying height, age, and sex as top predictors offers critical insights into the demographic and physiological factors that clinicians should consider when evaluating patients' risk profiles.