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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국경영과학회 경영과학 經營科學 第41卷 第1號
- 발행연도
- 2024.3
- 수록면
- 51 - 70 (20page)
- DOI
- 10.7737/KMSR.2024.41.1.051
이용수
초록· 키워드
오류제보하기
This study presents a model for estimating healthy life expectancy in Gyeongsangbuk-do at the city, county, and district level using machine learning. Quality-adjusted life expectancy (QALE) was calculated at each level using Graville correction and life tables. Next, 43 factors related to healthy life expectancy, including demographic and health care policy variables, were obtained from national health data. Machine learning was used to estimate healthy life expectancy. It was confirmed that LightGBM and artificial neural network had superior estimation performance compared to the multiple linear regression model commonly used in healthcare and medical science. Using the artificial neural network model with the best performance, we conducted additional factor analysis using Shapley additive explanations. Our findings confirmed that the depression experience rate and perceived stress rate were the most significant factors affecting healthy life expectancy in all cities, counties, and districts in Gyeongsangbuk-do. However, the sensitivity analysis revealed that the ranking of factors causing an increase or decrease in healthy life expectancy varied across cities, counties, and districts. Thus, it was confirmed that tailored policies, accounting for regional circumstances, are necessary to promote health and enhance equity.
목차
Abstract
1. 서론
2. 선행 연구
3. 방법론
4. 연구 결과
5. 결론
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