Lee Jaehyeong, Yoon Yourim, Kim Jiyoun, Kim Yong-Hyuk
Department of IT Convergence, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.
Department of Computer Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.
Biomimetics (Basel). 2024 Mar 15;9(3):179. doi: 10.3390/biomimetics9030179.
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.
本研究探讨基于元启发式算法的特征选择在提高机器学习诊断肌肉减少症性能方面的功效。在将机器学习应用于肌肉减少症诊断时，提取和利用对诊断效果有显著影响的特征成为一个关键方面。本研究利用第八次韩国老年人纵向研究（KLoSA）的数据，考察和谐搜索（HS）和遗传算法（GA）进行特征选择。对所得特征集的评估涉及决策树、随机森林、支持向量机和朴素贝叶斯算法。结果，用支持向量机训练的HS衍生特征集的准确率为0.785，加权F1分数为0.782，优于传统方法。这些发现凸显了基于元启发式算法选择的竞争优势，证明了其在推进肌肉减少症诊断方面的潜力。本研究主张在未来的肌肉减少症研究中进一步探索基于元启发式算法的特征选择的关键作用。
