biostudies-literatureUnknown2022Liu L<h4>Aim</h4>This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.<h4>Methods</h4>A total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we Computational and mathematical methods in medicine5005195https://www.ebi.ac.uk/biostudies/studies/S-EPMC9529423biostudies-literaturePredictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods.PMC9529423Liu LZhang XZhu MMCai LLfalsePredictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods.<h4>Aim</h4>This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.<h4>Methods</h4>A total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we 2022-01-01T00:00:00Z20222025-04-19T10:01:38.446Z2025-04-19T10:01:38.446ZS-EPMC95294233619976610.1155/2022/5005195