通过生理和功能指标对中老年人群进行分类

Veysel Alcan Ph.D
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引用次数: 0

摘要

衰老通过引起代谢、步态、平衡和肌肉功能的逐渐变化来影响个体的功能能力。确定中年(45-64岁)和老年人(≥65岁)之间的这些变化对于理解衰老的生物学和功能效应至关重要。本研究旨在通过使用机器学习(ML)方法分析代谢指标、步态参数、平衡测量和肌肉功能,以客观和可扩展的方式评估中老年人之间的差异。在这项研究中,使用了MIDUS数据集中的57个高维变量,包括步态参数(如步态速度、节奏、周期时间)、肌肉功能、平衡测量(如路径长度、摆动面积)、骨矿物质密度和生物电阻抗谱标记。使用监督ML模型对年龄组进行分类:偏最小二乘判别分析(PLS-DA)、主成分分析-线性判别分析(PCA-LDA)、支持向量机(SVM)和k-近邻(k-NN)。采用威尼斯盲交叉验证法评价模型的性能。其中SVM对训练数据的分类准确率最高(87%),对测试数据的分类准确率最高(77%)。PLS-DA模型的训练准确率为82%,测试准确率为86%。虽然k-NN模型在训练中显示出87%的准确率,但在测试中下降到68%。在敏感性和特异性值方面,SVM表现最佳(96%的敏感性,67%的特异性-训练;86%的敏感性,55%的特异性-测试),而PLS-DA和PCA-LDA模型表现出类似的趋势。结果表明,步行速度、节奏和平衡测量对年龄组歧视有显著贡献。这些发现突出了神经肌肉和生理因素在衰老导致的功能衰退中的作用,展示了基于机器学习的分类在衰老研究中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classificating middle-aged and older adults through physiological and functional measures
Aging affects the functional capacity of individuals by causing gradual changes in metabolic, gait, balance and muscle functions. Identifying these changes between middle-aged (45–64) and older (≥65) adults is critical to understanding the biological and functional effects of aging. This study aims to evaluate the differences between middle-aged and older adults in an objective and scalable manner by analyzing metabolic indicators, gait parameters, balance measurements and muscle functions using machine learning (ML) methods. In this study, 57 high-dimensional variables from the MIDUS dataset including gait parameters (e.g. gait speed, cadence, cycle time), muscle function, balance measurements (e.g. path length, swing area), bone mineral density and bioelectrical impedance spectroscopy markers were used. Supervised ML models were applied to classify the age groups: Partial Least Squares Discriminant Analysis (PLS-DA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). Venetian blind cross-validation approach was applied to evaluate the model performance. Among the models, SVM showed the highest classification accuracy (87 %) on the training data and 77 % accuracy on the testing data. PLS-DA model achieved 82 % accuracy in training and 86 % in testing. While k-NN model showed 87 % accuracy in training, it dropped to 68 % in testing. In terms of sensitivity and specificity values, SVM showed the best performance (96 % sensitivity, 67 % specificity - training; 86 % sensitivity, 55 % specificity - test), while PLS-DA and PCA-LDA models exhibited similar trends. The results show that walking speed, cadence, and balance measurements provide significant contributions to age group discrimination. These findings highlight the role of neuromuscular and physiological factors in functional decline due to aging, demonstrating the potential of machine learning-based classification in aging research.
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