| Objective To develop a risk prediction model leveraging demographic and routine laboratory parameters to facilitate early identification of individuals at high risk for osteoporosis during standard health examinations. Methods This study enrolled 2,173 participants who underwent health screenings and bone mineral density testing at our institution between January and November 2024. Demographic data, laboratory results, and bone mineral density measurements were analyzed. Propensity score matching (PSM) was applied to balance age and sex distributions, followed by univariate analysis to preliminarily adjust for confounding effects. Key predictors were selected using LASSO regression in the training cohort to construct a nomogram model. A dynamic interactive web-based prediction tool was subsequently developed. Model performance was evaluated using the validation cohort. Results The study included 153 osteoporosis cases (7.0%) and 2,020 non-osteoporosis controls. Post-PSM, age and sex distributions were balanced between groups (P >0.05). Univariate and machine learning analyses identified nine independent predictors: age, sex, body mass index, hemoglobin, platelet count, lymphocyte count, urinary nitrite, urine pH, and AST/ALT ratio. The model demonstrated robust discrimination, with AUCs of 0.863 (95% CI: 0.830–0.896) in the training set and 0.892 (95% CI: 0.840–0.943) in the validation set. An interactive online tool (https://healthmanagementcenter. shinyapps.io/osteoporosis_predictor/) was implemented, enabling personalized risk stratification at a predefined threshold of 9.03%. Conclusion The model integrating PSM and machine learning effectively identifies high-risk osteoporosis populations using routinely available data, highlighting its potential for clinical implementation in preventive care strategies. |