Data_Sheet_1_Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023.pdf

IntroductionPhysical 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.MethodsThis 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/height2 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 (k = 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.ResultsThe 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.ConclusionThe 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.

引言：体适能被视为肌肉减少症的重要指标。本研究旨在开发并评估一种深度学习模型，以预测潜在肌肉减少症患者因肌肉减少症导致的体适能下降。方法：本研究采用了2010-2023年韩国国民体适能奖数据，数据包含65岁以上韩国人的运动和健康相关测量，包括身体成分和体适能变量。通过计算四肢肌肉量（ASM）与身高的平方之比（ASM/height^2）来定义正常和可能的肌肉减少症。深度学习模型采用EarlyStopping和ModelCheckpoint技术以防止过拟合，并通过分层k折交叉验证（k=5）进行评估。模型在每个折叠中使用训练数据和验证数据进行训练和测试。模型性能通过混淆矩阵、受试者工作特征曲线和曲线下面积进行评估。确定每个交叉验证的平均性能指标。为了分析特征重要性，使用了SHAP、置换特征重要性和LIME作为模型无关的解释方法。结果：深度学习模型在区分肌肉减少症方面证明是有效的，准确率为87.55%，精确率为85.57%，召回率为90.34%，F1分数为87.89%。腰围（WC，厘米）、绝对握力（kg）和体脂率（BF，%）对模型输出有影响。SHAP、LIME和置换特征重要性分析表明，腰围和绝对握力是最重要的变量。腰围、八字体行走、体脂率、计时站立和坐位伸展成为预测可能肌肉减少症的关键因素。结论：深度学习模型在预测可能肌肉减少症方面表现出高准确率和召回率。鉴于开发更详细、更准确的肌肉减少症预测模型的必要性，本研究的结果为利用深度学习提高肌肉减少症预测能力提供了希望。