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Abstract：
This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection\'s pivotal role in future sarcopenia research.
摘要：
本研究探讨了基于元启发式的特征选择在提高诊断肌肉减少症的机器学习性能方面的功效。在将机器学习应用于肌肉减少症诊断时，显着影响诊断效能的特征的提取和利用成为关键方面。使用第八届韩国衰老纵向研究(KLoSA)的数据，这项研究检查了和声搜索（HS）和遗传算法（GA）的特征选择。对结果特征集的评估涉及决策树，随机森林，支持向量机，和幼稚的贝叶斯算法。因此,用支持向量机训练的HS衍生特征集的准确度为0.785,加权F1得分为0.782,优于传统方法.这些发现强调了基于元启发式的选择的竞争优势，证明其在推进肌少症诊断方面的潜力。本研究主张进一步探索基于元启发式的特征选择在未来的肌少症研究中的关键作用。
