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Lee, Jea-Young;Kim, Hyun-Ji
- Journal of the Korean Data and Information Science Society
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- v.25 no.2
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- pp.373-384
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- 2014
Data mining is to clarify pattern or correlation of mass data of complicated structure and to predict the diverse outcomes. This technique is used in the fields of finance, telecommunication, circulation, medicine and so on. In this paper, we selected risk factors of respiratory diseases in the field of medicine. The data we used was divided into respiratory diseases group and health group from the Gyeongsangbuk-do database of Community Health Survey conducted in 2012. In order to select major risk factors, we applied data mining techniques such as neural network, logistic regression, Bayesian network, C5.0 and CART. We divided total data into training and testing data, and applied model which was designed by training data to testing data. By the comparison of prediction accuracy, CART was identified as best model. Depression, smoking and stress were proved as the major risk factors of respiratory disease.
https://doi.org/10.7465/jkdi.2014.25.2.373 인용 PDF KSCI

임종명;이현석;장미숙;이진홍
- Proceedings of the Korea Air Pollution Research Association Conference
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- 2002.11a
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- pp.197-198
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- 2002
황사는 장거리를 이동해 한반도 둥에 영향을 미치는데, 호흡기 질환, 눈 질환, 알레르기 등 각종 질환을 유발하고, 최근에는 중국의 산업화에 따라 납, 카드뮴 같은 독성금속과 발암물질 둥 유해 오염물질까지 포함하고 있어 건강에 위협을 주고 있다. 나아가 황사에 포함된 미세 입자들이 대기 중에서 화학반응을 일으켜 각종 산화물을 생성하는 까닭에 만성기관지염을 악화시키고, 노인과 영아의 호흡기 질환을 유발하기도 한다 그밖에 황사는 햇빛을 차단해 시정거리를 감소시키고 항공기 운항에도 영향을 미친다. (중략)
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