A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults

In this study, we discovered that when predicting osteoporosis,a number of machine learning techniques fell short of logistic regression. In a specific population, we have innovatively developed a risk prediction model for osteoporosis events that integrates genetic and environmental factors, is an effective tool for assessing osteoporosis risk and can serve as the basis for specialized intervention approaches.