| Abstrakt: |
A recent study conducted at Quzhou Affiliated Hospital of Wenzhou Medical University in China has developed a prediction model for identifying osteoporosis in men aged 50 and older. The model utilized machine learning algorithms and key predictors such as age, body mass index, poverty income ratio, serum calcium, and race. The random forest regressor emerged as the most effective model, achieving a coefficient of determination of 0.218. The study suggests that this predictive model can aid in the early detection of osteoporosis and potentially reduce the incidence of fractures in this high-risk population. [Extracted from the article] |
