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논문검색

Technology Convergence (TC)

Prediction of Childhood Asthma Using Expectation Maximization and Minimum Description Length Algorithm

초록

영어

Due to the recent rapid industrialization worldwide, the number of pediatric asthma patients is increasing. And the fine dust containing heavy metals is linked to the characteristics of high toxic lead due to the increase heating in factory operation and automobile driving. It is the reason of arsenic increasing. In the treatment of pediatric asthma patients, drug administration, oral drug entry, and HMPC (Home Management Plan of Care) are used. In this paper, we analyze the relationship between the onset of asthma and the method of prescription for specific childhood asthma in the United States using EM (Expectation Maximization) and MDL (Minimum Description Length) algorithms. And the association is also analyzed by comparing the nature of specific congestion between the past prevalence of digestive asthma and the recent prevalence of environmental pollution.

목차

Abstract
I. Introduction
II. Related Literature
2.1 Clustering
2.2 EM (Expectation Maximization ) algorithm
2.3 MDL (Minimum Description Length) Algorithm
III. Experimental Process
3.1 Childhood Asthma
3.2 Experimental data description
3.3 Preprocess
IV. Evaluation and Discussion
4.1 Experimental Result
4.2 Evaluation and Discussion
V. Conclusion
References

저자정보

  • Hyo Seon Kim Master’s student, Dept. of Medical IT Marketing, Eulji University, Korea
  • Jong Suk Park Deputy CEO, CEO}, PURIUM Co. Ltd., Korea
  • Dong Kyu Nam Deputy CEO, CEO}, PURIUM Co. Ltd., Korea
  • Yong Gyu Jung Professor, Dept. of Medical IT, Eulji University, Korea

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