目标:随着老年人口规模的逐渐增加,肌肉骨骼疾病,比如肌肉减少症,正在增加。诊断技术,如X射线,计算机断层扫描,磁共振成像用于预测和诊断肌肉减少症,和使用机器学习的方法正在逐渐增加。这项研究旨在创建一个模型,可以使用身体特征和活动相关变量来预测肌肉减少症,而无需医疗诊断设备。例如成像设备,适用于60岁或以上的老年人。
方法:使用从韩国国家健康和营养检查调查获得的公开数据构建了肌少症预测模型。使用Logistic回归建立模型,支持向量机(SVM)XGBoost,LightGBM,RandomForest,和多层感知器神经网络(MLP)算法,以及用算法训练的模型的特征重要性,除了SVM和MLP,被分析。
结果:用LightGBM算法构建的肌少症预测模型取得了最高的检验精度,0.848在构建LightGBM模型时,身体特征变量,如体重指数,体重,腰围显示出很高的重要性,和活动相关变量也被用于构建模型。
结论:肌少症预测模型,只包括身体特征和活动相关因素,表现出优异的性能。该模型有可能帮助老年人早期发现肌肉减少症,特别是在获得医疗资源或设施有限的社区。GeriatrGerontolInt2024;••:••-•。
OBJECTIVE: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X-rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity-related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older.
METHODS: A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi-layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed.
RESULTS: The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity-related variables were also used in constructing the model.
CONCLUSIONS: The sarcopenia prediction model, which consisted of only physical characteristics and activity-related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; 24: 595-602.