Prediction of Osteoporosis

This paper explores the use of AI for evaluating and predicting osteoporosis (OP) risk factors in both men and women, as well as recommending suitable sports programs for treatment. Data from 1224 patients was analyzed using eight AI algorithms, including decision tree, random forest, and gradient boosting, with the best performance achieved by the FR algorithm (AUROC 0.91) for men and the GB algorithm (AUROC 0.95) for women. The RF algorithm showed the highest accuracy in recommending exercise programs, with AUROC values of 0.96 for women and 0.99 for men, indicating that AI can effectively classify osteoporosis risk and suggest personalized treatment options.