The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Data Preprocessing for Predicting Sarcopenia Based on Machine Learning

기계학습 기반 근감소증 예측을 위한 데이터 전처리 기법

  • 최윤 (가천대학교 컴퓨터공학과 ) ;
  • 윤유림 (가천대학교 컴퓨터공학과)
  • Received : 2023.03.30
  • Accepted : 2023.05.08
  • Published : 2023.05.31
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Abstract

Sarcopenia is an increasingly common disease among the elder that has recently received attention. Although the causes of sarcopenia are diverse, aging, dietary habits, lack of exercise are the one of the major factors. As the causes of sarcopenia are diverse, it is important to develop strategies for prevention and treatment. However, predicting sarcopnia accuartely is difficult due to the variety of factors involved. Here, machine learning can significantly improve the accuracy and convenience of predicting sarcopenia. However, since lifestyle habits and biological data are vast, using data without preprocessing may be inappropriate in terms of time complexity and accuracy. This paper reviews recent literature on sarcopnia and its causes, focusing on preprocessing the data to be used in sarcopnia prediction machine learning accrodingly.

근감소증은 노인들 사이에서 점점 더 흔하게 발생하고 있어, 최근 주목을 받고 있는 질병이다. 근감소증의 원인은 매우 다양하게 나타나지만, 노화, 식습관, 운동 부족등이 주요한 원인들 중 하나이다. 근감소증은 원인이 다양한 만큼 예방 및 치료에 전략을 개발하는 것이 중요하다. 하지만 요인이 다양한 만큼 사람이 근감소증을 정확하게 예측하기는 어렵다. 여기서 기계학습을 이용해 근감소증 예측의 정확도와 편의를 크게 높일 수 있다. 그러나 생활습관과 생체 데이터의 양은 방대한 만큼, 전처리 없이 데이터를 쓰기에는 시간복잡도와 정확성 측면에서 부적절할 수 있다. 본 논문에서는 근감소증과 그 원인에 대한 최신 문헌을 검토하고, 그에 맞게 기계학습 기만 근감소증 예측에 활용할 데이터를 전처리하는데 초점을 맞춘다.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부, 교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No.2022R1F1A1066017, NRF-2022S1A5C2A07090938)

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