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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2017 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.9 No.1
- 발행연도
- 2017.6
- 수록면
- 69 - 72 (4page)
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초록· 키워드
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This paper presents an extraction method for major adverse cardiac events (MACE) from KAMIR dataset based on deep learning algorithm. The KAMIR dataset has been under construction at 41 Primary PCI Centers in Korea since November 2005. Many studies for the KAMIR have proceeded via analysis of statistical approaches: student’s t-test, χ²-test, and multivariate logistic regression analysis, nominal Gini-Index, etc. And also, there have been a lot of studies for various area using deep learning algorithms. Unfortunately, only few studies on deep learning for nominal feature extraction have as yet been completed, and the KAMIR dataset has problems in extracting representative features remain for 1) unbalanced dataset for classes, 2) instances having almost all of the features of the datasets, and 3) instances having almost all features with non-null values. Thus, we considered deep learning algorithm for extraction of major features among 7 feature groups from KAMIR, excluded the features having null or none values. In this paper, we propose here an extraction method for major adverse cardiac events (MACE) from KAMIR dataset using deep learning algorithm. Therefore, we can have good major features of AMI patients from the KAMIR, and it can select the major features for given conditions.
목차
Abstract
I. INTRODUCTION
II. DEEP LEARNING FOR EXTRACTION OF MAJOR FEATURES
III. EXPERIMENTAL DATASET AND RESUTLS
IV. DISCUSSION AND CONCLUSIONS
REFERENCES
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