Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Soo-Kyoung | - |
dc.contributor.author | Son, Youn-Jung | - |
dc.contributor.author | Kim, Jeongeun | - |
dc.contributor.author | Kim, Hong-Gee | - |
dc.contributor.author | Lee, Jae-Il | - |
dc.contributor.author | Kang, Bo-Yeong | - |
dc.contributor.author | Cho, Hyeon-Sung | - |
dc.contributor.author | Lee, Sungin | - |
dc.date.accessioned | 2021-08-11T23:24:16Z | - |
dc.date.available | 2021-08-11T23:24:16Z | - |
dc.date.issued | 2014-04 | - |
dc.identifier.issn | 2093-3681 | - |
dc.identifier.issn | 2093-369X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/12321 | - |
dc.description.abstract | Objectives: The purposes of this study were to identify the factors that affect the health-related quality of life (HRQoL) of the elderly with chronic diseases and to subsequently develop from such factors a prediction model to help identify HRQoL risk groups that require intervention. Methods: We analyzed a set of secondary data regarding 716 individuals extracted from the Korea National Health and Nutrition Examination Survey from 2008 to 2010. The statistical package of SPSS and MATLAB were used for data analysis and development of the prediction model. The algorithms used in the study were the following: stepwise logistic regression (SLR) analysis and machine learning (ML) techniques, such as decision tree, random forest, and support vector machine methods. Results: Five factors with statistical significance were identified for HRQoL in the elderly with chronic diseases: 'monthly income', 'diagnosis of chronic disease', 'depression', 'discomfort', and 'perceived health status.' The SLR analysis showed the best performance with accuracy = 0.93 and F-score = 0.49. The results of this study provide essential materials that will help formulate personalized health management strategies and develop interventions programs towards the improvement of the HRQoL for elderly people with chronic diseases. Conclusions: Our study is, to our best knowledge, the first attempt to identify the influencing factors and to apply prediction models for the HRQoL of the elderly with chronic diseases by using ML techniques as an alternative and complement to the traditional statistical approaches. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 대한의료정보학회 | - |
dc.title | Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.4258/hir.2014.20.2.125 | - |
dc.identifier.scopusid | 2-s2.0-84900003751 | - |
dc.identifier.wosid | 000219426200007 | - |
dc.identifier.bibliographicCitation | Healthcare Informatics Research, v.20, no.2, pp 125 - 134 | - |
dc.citation.title | Healthcare Informatics Research | - |
dc.citation.volume | 20 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 125 | - |
dc.citation.endPage | 134 | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART001875075 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordAuthor | Quality of Life | - |
dc.subject.keywordAuthor | Aged | - |
dc.subject.keywordAuthor | Chronic Disease | - |
dc.subject.keywordAuthor | Artificial Intelligence | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.