인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
학술저널
Full-text AI
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics.
Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 ㎏/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting.
Results: Prediction accuracy was increased from 1<SUP>st</SUP> to 2<SUP>nd</SUP> and 3<SUP>rd</SUP> analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1<SUP>st</SUP> (54.69%), 2<SUP>nd</SUP> (73.52%), and 3<SUP>rd</SUP> (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1<SUP>st</SUP> (0.640), 2<SUP>nd</SUP> (0.816), and 3<SUP>rd</SUP> (0.939) prediction analysis based on reduced weight.
Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.
Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 ㎏/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting.
Results: Prediction accuracy was increased from 1<SUP>st</SUP> to 2<SUP>nd</SUP> and 3<SUP>rd</SUP> analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1<SUP>st</SUP> (54.69%), 2<SUP>nd</SUP> (73.52%), and 3<SUP>rd</SUP> (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1<SUP>st</SUP> (0.640), 2<SUP>nd</SUP> (0.816), and 3<SUP>rd</SUP> (0.939) prediction analysis based on reduced weight.
Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2020-519-000679976
