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학술지 Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases Using Machine Learning Techniques
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저자
이수경, 손연정, 김정은, 김홍기, 이재일, 강보경, 조현성, 이성인
발행일
201404
출처
Healthcare Informatics Research, v.20 no.2, pp.125-134
ISSN
2093-3681
출판사
Korean Society of Medical Informatics
DOI
https://dx.doi.org/10.4258/hir.2014.20.2.125
협약과제
13PC1200, 맞춤형 모바일 지식 서비스를 위한 지식스토어 핵심기술 개발, 박윤경
초록
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. © 2014 The Korean Society of Medical Informatics.
키워드
Aged, Artificial intelligence, Chronic disease, Quality of life
KSP 제안 키워드
Best performance, Data analysis, Decision Tree(DT), Elderly People, F-score, Health-related quality of life, Influencing Factors, Logistic Regression(LR), Machine Learning technique(MLT), Machine learning (ml), Management strategy