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A Design of a Patient-customized Healthcare System based on the Hadoop with Text Mining (PHSHT) for an efficient Disease Management and Prediction
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- 발행기관
- 보안공학연구지원센터(IJSEIA) 바로가기
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- 간행물
- International Journal of Software Engineering and Its Applications SCOPUS 바로가기
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- 통권
- Vol.8 No.8 (2014.08)바로가기
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- 페이지
- pp.131-150
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A230690
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- This paper proposes a design of a Patient-customized Healthcare System based on the Hadoop with Text Mining (PHSHT) for an efficient Disease Management and Prediction. The PHSHT consists of a Medical Data Collection Module (MDCM), a Text Mining based Hadoop Module (TMHM), a Disease Rules Creation Module (DRCM), and a Disease Management and Prediction Module (DMPM). The MDCM stores the big data like healthcare and medical information in the Hbase. The TMHM converts the unstructured data collected by MDCM to the structured data and stores the result in the Hbase. The DRCM generates disease rules by using the Conditional Probability Set Theory (CPST) proposed in this paper and stores them in the Hbase. The DMPM not only provides an efficient patient-customized medical service and information by comparing the disease rules generated by the DRCM with a patient’s information, a patient’s current status, and family history but also predicts a patient’s disease according to his health status. In particular, because the CPST generates the disease rules by complementing the problems of an existing C4.5 algorithm which does not consider the relation between attributes, this paper guarantees that it excels the C4.5 algorithm.
목차
Abstract
1. Introduction
2. Related Works
2.1. Text Mining
2.2 Hadoop
2.3. Hbase
2.4. RST (Rough Set Theory)
2.5. A C4.5 algorithm
3. A PHSHT design
3.1 A design of Medical Data Collection Module (MDCM)
3.2 A design of Text Mining based Hadoop Module (TMHM)
3.3 A design of Disease Rule Creation Module (DRCM)
3.4 A design of Disease Management and Prediction Module (DMPM)
4. Analysis
4.1 The accuracy estimation for disease rules
4.2 The analysis of risk index
5. Conclusion
References
키워드
Text Mining Disease Rules Conditional Probability Set Theory Disease Management Disease Prediction Hadoop MapReduce Hbase저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Software Engineering and Its Applications
- 간기
- 월간
- pISSN
- 1738-9984
- 수록기간
- 2008~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.8 No.8
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