Ajou University repository

모바일 헬스 서비스 사용자 특성 분석 및 이탈 예측 모델 개발
Citations

SCOPUS

0

Citation Export

DC Field Value Language
dc.contributor.author한정현-
dc.contributor.author이주연-
dc.date.issued2021-12-
dc.identifier.issn1738-480X-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/37590-
dc.identifier.urihttps://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002794812-
dc.description.abstractAs the average life expectancy is rising, the population is aging and the number of chronic diseases is increasing. This has increased the importance of healthy life and health management, and interest in mobile health services is on the rise thanks to the development of ICT(Information and communication technologies) and the smartphone use expansion. In order to meet these interests, many mobile services related to daily health are being launched in the market. Therefore, in this study, the characteristics of users who actually use mobile health services were analyzed and a predictive model applied with machine learning modeling was developed. As a result of the study, we developed a prediction model to which the decision tree and ensemble methods were applied. And it was found that the mobile health service users' continued use can be induced by providing features that require frequent visit, suggesting achievable activity missions, and guiding the sensor connection for user’s activity measurement.-
dc.language.isoKor-
dc.publisher한국시스템엔지니어링학회-
dc.title모바일 헬스 서비스 사용자 특성 분석 및 이탈 예측 모델 개발-
dc.title.alternativeMobile health service user characteristics analysis and churn prediction model development-
dc.typeArticle-
dc.citation.endPage105-
dc.citation.number2-
dc.citation.startPage98-
dc.citation.title시스템엔지니어링학술지-
dc.citation.volume17-
dc.identifier.bibliographicCitation시스템엔지니어링학술지, Vol.17 No.2, pp.98-105-
dc.identifier.doi10.14248/JKOSSE.2021.17.2.098-
dc.subject.keywordHealthcare-
dc.subject.keywordHealth Management-
dc.subject.keywordMobile Health-
dc.subject.keywordMachine Learning-
dc.subject.keywordSupervised Learning-
dc.type.otherArticle-
Show simple item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Lee, Joo Yeoun Image
Lee, Joo Yeoun이주연
Department of Industrial Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.