Comparison of predictive models for knee pain and analysis of individual and physical activity variables using interpretable machine learning

IF 1.6 4区 医学 Q3 ORTHOPEDICS
Knee Pub Date : 2025-03-04 DOI:10.1016/j.knee.2025.02.006
Jun-hee Kim
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引用次数: 0

Abstract

Background

Knee pain is associated with not only individual factors such as age and obesity but also physical activity factors such as occupational activities and exercise, which has a significant impact on the lives of adults and the elderly.

Objectives

The purpose of this study was to construct a model for predicting knee pain using individual and physical activity variables and to determine the relationship between knee pain and individual and physical activity variables.

Design

Observational study.

Methods

A total of 19 variables related to individual and physical activity were used to create a knee pain prediction model. Model composition variables were selected using recursive feature elimination with cross validation. The performance of the model was evaluated using test data, and the relationship between knee pain and predictor variables was analyzed using SHapley Additive exPlanations (SHAP).

Results

The CatBoost model showed the highest performance. And, activity limitation was identified as the most influential predictor, followed by weekly physical activity, body image, weight change, occupational type, age, BMI, and housing type.

Conclusion

Knee pain prediction models built with individual and physical activity variables can exhibit relatively high predictive performance, and interpretable machine learning models can provide valuable insight into the complex relationships between individual and physical activity variables and knee pain.
膝关节疼痛预测模型的比较,以及使用可解释机器学习对个体和身体活动变量的分析
膝关节疼痛不仅与年龄和肥胖等个人因素有关,还与职业活动和锻炼等身体活动因素有关,这对成年人和老年人的生活都有重大影响。目的构建个体和身体活动变量预测膝关节疼痛的模型,并确定个体和身体活动变量与膝关节疼痛的关系。DesignObservational研究。方法采用与个体和体力活动相关的19个变量建立膝关节疼痛预测模型。采用递归特征消除和交叉验证选择模型组成变量。使用测试数据评估模型的性能,并使用SHapley加性解释(SHAP)分析膝关节疼痛与预测变量之间的关系。结果CatBoost模型表现出最高的性能。并且,活动限制被确定为最具影响力的预测因子,其次是每周体力活动,身体形象,体重变化,职业类型,年龄,BMI和住房类型。结论基于个体和身体活动变量构建的膝关节疼痛预测模型具有较高的预测性能,可解释的机器学习模型可以为了解个体和身体活动变量与膝关节疼痛之间的复杂关系提供有价值的见解。
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来源期刊
Knee
Knee 医学-外科
CiteScore
3.80
自引率
5.30%
发文量
171
审稿时长
6 months
期刊介绍: The Knee is an international journal publishing studies on the clinical treatment and fundamental biomechanical characteristics of this joint. The aim of the journal is to provide a vehicle relevant to surgeons, biomedical engineers, imaging specialists, materials scientists, rehabilitation personnel and all those with an interest in the knee. The topics covered include, but are not limited to: • Anatomy, physiology, morphology and biochemistry; • Biomechanical studies; • Advances in the development of prosthetic, orthotic and augmentation devices; • Imaging and diagnostic techniques; • Pathology; • Trauma; • Surgery; • Rehabilitation.
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