Able-Art Sport, Department of Theory, Hyupsung University, Hwaseong, Gyeonggi-do, Republic of Korea.
Department of Physical Education, Seoul National University, Seoul, Republic of Korea.
Front Public Health. 2023 Aug 8;11:1241388. doi: 10.3389/fpubh.2023.1241388. eCollection 2023.
Physical fitness is regarded as a significant indicator of sarcopenia. This study aimed to develop and evaluate a deep-learning model for predicting the decline in physical fitness due to sarcopenia in individuals with potential sarcopenia.
This study used the 2010-2023 Korean National Physical Fitness Award data. The data comprised exercise- and health-related measurements in Koreans aged >65 years and included body composition and physical fitness variables. Appendicular muscle mass (ASM) was calculated as ASM/height to define normal and possible sarcopenia. The deep-learning model was created with EarlyStopping and ModelCheckpoint to prevent overfitting and was evaluated using stratified k-fold cross-validation ( = 5). The model was trained and tested using training data and validation data from each fold. The model's performance was assessed using a confusion matrix, receiver operating characteristic curve, and area under the curve. The average performance metrics obtained from each cross-validation were determined. For the analysis of feature importance, SHAP, permutation feature importance, and LIME were employed as model-agnostic explanation methods.
The deep-learning model proved effective in distinguishing from sarcopenia, with an accuracy of 87.55%, precision of 85.57%, recall of 90.34%, and F1 score of 87.89%. Waist circumference (WC, cm), absolute grip strength (kg), and body fat (BF, %) had an influence on the model output. SHAP, LIME, and permutation feature importance analyses revealed that WC and absolute grip strength were the most important variables. WC, figure-of-8 walk, BF, timed up-and-go, and sit-and-reach emerged as key factors for predicting possible sarcopenia.
The deep-learning model showed high accuracy and recall with respect to possible sarcopenia prediction. Considering the need for the development of a more detailed and accurate sarcopenia prediction model, the study findings hold promise for enhancing sarcopenia prediction using deep learning.
身体机能被视为肌肉减少症的重要指标。本研究旨在开发和评估一种深度学习模型，以预测有发生肌肉减少症风险的个体因肌肉减少症导致身体机能下降的情况。
本研究使用了 2010-2023 年韩国国民体质奖数据。该数据包含了韩国 65 岁以上人群的运动和健康相关测量值，包括身体成分和身体机能变量。四肢骨骼肌质量（ASM）按 ASM/身高计算，以定义正常和可能的肌肉减少症。深度学习模型采用 EarlyStopping 和 ModelCheckpoint 防止过拟合，并使用分层 k 折交叉验证（=5）进行评估。模型使用来自每个折叠的训练数据和验证数据进行训练和测试。使用混淆矩阵、接收者操作特征曲线和曲线下面积评估模型性能。从每个交叉验证中确定平均性能指标。为了分析特征重要性，使用了 SHAP、置换特征重要性和 LIME 作为与模型无关的解释方法。
深度学习模型在区分肌肉减少症方面表现出色，准确率为 87.55%，精密度为 85.57%，召回率为 90.34%，F1 得分为 87.89%。腰围（WC，厘米）、绝对握力（kg）和体脂肪（BF，%）对模型输出有影响。SHAP、LIME 和置换特征重要性分析表明，WC 和绝对握力是最重要的变量。WC、八字走、BF、计时起立行走和坐立前伸是预测可能发生肌肉减少症的关键因素。
深度学习模型在预测可能发生的肌肉减少症方面具有较高的准确率和召回率。考虑到需要开发更详细和准确的肌肉减少症预测模型，本研究结果为使用深度学习增强肌肉减少症预测提供了希望。
