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The Prediction of Unmet Healthcare Needs after Sample Correction

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  • 발행기관
    한국EA학회 바로가기
  • 간행물
    정보화연구 KCI 등재 바로가기
  • 통권
    제18권 1호 (2021.03)바로가기
  • 페이지
    pp.17-24
  • 저자
    Choi, Youngjin
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A392158

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초록

영어
With the change in population structure, small-scale households have rapidly progressed, and since 2015, single-person households have emerged as the main household type. Along with the change in the structure of the household type, health management of single-person households is on the rise. Therefore, this study analyzed the factors influencing the classification of unmet medical care by household type using the 13th data of the Korea Welfare Panel Study (KOWEPS) in 2018. Considering that the proportion of unsatisfied medical experience is unbalanced data, which is 0.8% of the total sample, the performance of the classification analysis algorithms was compared after correcting the sample using the resampling method. As a result of the study, it can be said that the use of the random oversampling technique is superior to that of the random undersampling. In addition, in the recent classification analysis, the performance of classification analysis has been improved by using an ensemble technique such as XGBoost. In this study, it was difficult to find a difference from the classification analysis performance of the traditional classification analysis method such as logistic regression or SVM. As in the sample in this study, in a situation where 99% of the unsatisfied medical care workers are unexperienced, even if the prediction results using the algorithm are all unexperienced, it has a loophole that the accuracy reaches 99%. Therefore, in this study, in order to improve the accuracy of classification of unmet medical experience, a more valid model was derived by solving the imbalance problem and performing classification analysis. This study is meaningful in that it proved that the analysis using the original data has limitations in samples containing multidimensional unbalanced data, such as unmet medical experience.

목차

Abstract
1. Introduction
2. Research Methods
2.1. Research Sample
2.2. Variables
3. Results
3.1. Sample Characteristics
3.2. Comparison of Classification Algorithms
4. Results
REFERENCES

키워드

Unmet Healthcare Needs Single-person household Panel Data Imbalanced Sample

저자

  • Choi, Youngjin [ Healthcare Management Dept., Eulji University ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국EA학회 [한국엔터프라이즈아키텍처학회]
  • 설립연도
    2002
  • 분야
    복합학>과학기술학
  • 소개
    한국EA학회는 전사적 관점의 아키텍처 개념 및 원칙을 국내 민간기업 및 정부기관에 적용 확산시키고, EA 및 관련 분야의 연구, 전문인력의 양성 및 정책적 건의 등을 통해 기업 및 정부기관의 경쟁력 및 생산성을 향상시키고, 우리나라 지식 기반 산업 등의 고도화를 도모하는 것을 목적으로 합니다.

간행물

  • 간행물명
    정보화연구 [정보화연구(구 정보기술아키텍처연구)]
  • 간기
    계간
  • pISSN
    1738-382X
  • 수록기간
    2004~2026
  • 등재여부
    KCI 등재
  • 십진분류
    KDC 325 DDC 658

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