Previous epidemiological studies have associated various body-related indicators with arthritis
however, the results have been inconclusive. Therefore, this research aimed to develop and validate a nomogram model for predicting the risk of arthritis using easily available indicators and to assess the model's predictive performance. Cross-sectional data were collected from 3660 participants in the 2021-2023 National Health and Nutrition Examination Survey. The research conducted variable selection and model development using the Least Absolute Shrinkage and Selection Operator regression model and multivariate logistic regression analysis, and the performance of the nomogram was validated. The nomogram model incorporated nine independent predictors: age, sex, family poverty-income ratio, race, diabetes status, vitamin D level, systemic immunity-inflammation index, and waist-to-height ratio. After validation, it has been proven that the nomogram model has good performance. The nomogram model developed in this study effectively predicts the risk probability of arthritis in the general population of the United States. All variables included in this nomogram can be easily obtained from the population.