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머신러닝 기반 무릎 골 관절염 심각도 분류를 위한 특징 중요도 분석

Other Titles
 Analysis of Feature Importance for Knee Osteoarthritis Severity Classification Using Machine Learning 
Authors
 유선국  ;  채동식  ;  신영철  ;  김성우 
Citation
 Journal of the Institute of Electronics and Information Engineers of Korea (전자공학회논문지), Vol.57(2) : 99-106, 2020-02 
Journal Title
 Journal of the Institute of Electronics and Information Engineers of Korea (전자공학회논문지) 
ISSN
 2287-5026 
Issue Date
2020-02
Abstract
In this study, the OsteoArthritis Initiative(OAI) dataset was used to evaluate four statistical methods to determine the importance of 6 clinical information and 18 X-ray image features for knee osteoarthritis severity classification. The Kellgren-Lawrence Grade, knee osteoarthritis seveirity classification scoring system was classified using a random forest method which used features of high importance. Joint space narrowing, osteophytes, and osteoosclerosis were ranked in the top five most important features in all methods. The model using 24 features had a balanced classification accuracy of 93.7%, while the model using 12 features including clinical information, joint space narrowing, and osteophytes, had a balanced classification accuracy of 93.4%. Both models had similar levels of accuracy, but calculation cost to obtain image features of the second model was lower than the first model. It used one-thirds of features of the first model and required the use of only 6 image features compared to the first methods required use of 18 image features.
Full Text
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002558297
DOI
10.5573/ieie.2020.57.2.99
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179009
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