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Developing a Risk Prediction System for Osteoporosis Using Machine Learning Models

Category: *Precision Health & Smart Medical

Exhibitor: NATIONAL DEFENSE MEDICAL UNIVERSITY

Booth No: 7

Characteristic

Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; however, it is expensive, requires trained personnel, and is not easily implemented in community settings. Our team developed an osteoporosis prediction system using machine learning techniques, which successfully predicts osteoporosis based on five easily obtainable health examination parameters—age, BMI, sex, body fat percentage, and serum creatinine—with an accuracy of up to 85.5%. This system can be applied to community health screenings to facilitate early detection and intervention.

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