Aim. This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals. Methods. 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 created a simplified nomogram based on logistic regression for better clinical application. Results. About 31.4% (1690) individuals were with self-reported knee pain. The logistic regression showed that female gender (odds ratio ), pain elsewhere (), and body mass index () were significantly associated with increased risk of knee pain. In the test set, the logistic regression () showed similar but slightly higher accuracy than the random forest (), while the performance of the Extreme Gradient Boosting model was less reliable (). Based on mean decrease accuracy, the most important first five predictions were pain elsewhere, waist circumference, body mass index, age, and gender. Additionally, the most important first five predictions with the highest mean decrease Gini index were pain elsewhere, body mass index, waist circumference, triglycerides, and age. The nomogram model showed good discrimination ability with an AUC of 0.75 (0.73-0.77), a sensitivity of 0.72, specificity of 0.71, a positive predictive value of 0.45, and a negative predictive value of 0.88. Conclusion. This study proposed a convenient nomogram tool to evaluate the risk of knee pain for the middle-aged and elderly US population in primary care. All the input variables can be easily obtained in a clinical setting, and no additional radiologic assessments were required.

中文翻译：

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基于机器学习方法的中老年人膝关节疼痛预测模型

瞄准。本研究使用机器学习方法开发了中老年人膝关节疼痛的预测模型。方法. 全国健康与营养调查共获得45岁以上人群5386人。参与者以 7:3 的比例随机分为训练集和测试集。训练集用于创建预测模型，而测试集用于验证所提出的模型。我们基于三种机器学习方法构建了多个预测模型：逻辑回归、随机森林和极端梯度提升。模型性能通过接受者下面积（AUC）、敏感性、特异性、阳性预测值和阴性预测值进行评估。此外，我们创建了一个基于逻辑回归的简化列线图，以实现更好的临床应用。结果. 大约 31.4% (1690) 的人自我报告有膝痛。逻辑回归显示女性性别（优势比),其他地方的疼痛 ()和体重指数 ()与膝关节疼痛风险增加显着相关。在测试集中，逻辑回归 ()与随机森林 (），而极限梯度提升模型的性能不太可靠（）。根据平均下降准确度，最重要的前五个预测是其他部位的疼痛、腰围、体重指数、年龄和性别。此外，平均降低基尼指数最重要的前五个预测是其他部位的疼痛、体重指数、腰围、甘油三酯和年龄。列线图模型显示出良好的辨别能力，AUC为0.75（0.73-0.77），敏感性为0.72，特异性为0.71，阳性预测值为0.45，阴性预测值为0.88。结论. 本研究提出了一种方便的列线图工具，用于评估美国中老年人在初级保健中的膝关节疼痛风险。所有输入变量都可以在临床环境中轻松获得，不需要额外的放射学评估。