Jiwoo Song  Jaehyeong Lee  Yourim Yoon

10.5626/JOK.2025.52.9.749

Sarcopenia KLoSA Machine Learning genetic algorithm Feature selection

Sarcopenia, a disease predominantly affecting the elderly, has emerged as a significant concern within the medical community. Due to the variety of sarcopenia's causes and diagnostic methods, identifying a specific cause remains challenging, which hampers the ability to predict it accurately with current knowledge. This study utilizes survey data from the Korean Longitudinal Study of Aging (KLoSA) to explore ways to enhance the accuracy of sarcopenia prediction through data preprocessing and feature selection using genetic algorithms. Data preprocessing reduced the number of features from 2,756 to 613. Subsequently, feature selection was performed and evaluated with logistic regression, XGBoost, and random forest as classification algorithms, achieving an accuracy of up to 84.73% and an F1-Score of 0.5953. These findings suggest practical insights into the effective application of genetic algorithms for analyzing survey-type data, potentially improving sarcopenia diagnosis.