이산화 기법을 이용한 심장병 수술의 예후예측

• 영문명: Predicting Prognosis of Cardiac Surgery using Discrete Methods
• 발행기관: 한국IT마케팅학회
• 저자명: 황윤성(Hwang Youn Sung) 정용규(Yong Gyu Jung)
• 간행물 정보: 『한국IT마케팅학회 논문집』한국IT마케팅학회 논문집 1권 1호, 106~108쪽, 전체 3쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2014.11.27

영문 초록

In particular, use of big data in the medical field improves the quality of services provided to the patient and reduce costs to the hospital. Moreover, it is useful in predicting the effectiveness of the various surgical procedures. Data mining can extract information on the basis of big data and can be used to exlore mass data efficiently. In data mining, there are some special technology and classification technique. Therefore, for efficient data mining, it is important to select an efficient algorithm from various special techniques and classification techniques. However, to select the appropriate classifiers and algorithms is a difficult problem. In this paper, I tried to experiment with special technology that can improve the classification performance through the Cardiac Surgery by Surgeon Beginning2008 of US Government's open data that has been collected in mortality after heart surgery. I compared accuracy of supervised discretization with accuracy of unsupervised discretization using the nearest neighborhood classifiers. As a tool for experiment, I used WEKA v3.6.1 developed by Waikato University.

목차

Abstract

Ⅰ. 서론

Ⅱ. 관련연구

Ⅲ. 실험

Ⅳ. 실험결과 고찰

Ⅴ. 결론

참고문헌

키워드

Cardiac Surgery K-nearest neighborhood classifiers(KNN) Supervised discretization Unsupervised discretization