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Random forests 기법을 이용한 백내장 예측모형 : 일개 대학병원 건강검진 수검자료에서

Other Titles
 A Prediction Model for the Development of Cataract Using Random Forests 
 한은정  ;  송기준  ;  김동건 
 Korean Journal of Applied Statistics (응용통계연구), Vol.22(4) : 771-780, 2009 
Journal Title
 Korean Journal of Applied Statistics (응용통계연구) 
Issue Date
Random Forests ; screening test ; prediction model of cataracts ; accuracy ; sensitivity
Cataract is the main cause of blindness and visual impairment, especially, age-related cataract accounts for about half of the 32 million cases of blindness worldwide. As the life expectancy and the expansion of the elderly population are increasing, the cases of cataract increase as well, which causes a serious economic and social problem throughout the country. However, the incidence of cataract can be reduced dramatically through early diagnosis and prevention. In this study, we developed a prediction model of cataracts for early diagnosis using hospital data of 3,237 subjects who received the screening test first and then later visited medical center for cataract check-ups cataract between 1994 and 2005. To develop the prediction model, we used random forests and compared the predictive performance of this model with other common discriminant models such as logistic regression, discriminant model, decision tree, naive Bayes, and two popular ensemble model, bagging and arcing. The accuracy of random forests was 67.16%, sensitivity was 72.28%, and main factors included in this model were age, diabetes, WBC, platelet, triglyceride, BMI and so on. The results showed that it could predict about 70% of cataract existence by screening test without any information from direct eye examination by ophthalmologist. We expect that our model may contribute to diagnose cataract and help preventing cataract in early stages.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Song, Ki Jun(송기준) ORCID logo https://orcid.org/0000-0003-2505-4112
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