Young Sun Kim

Publications (2)4.97 Total impact

• Article: Methodology of the thyroid gland disease decision-making using profiling in steroid hormone pathway.

  Young Sun Kim, Chang No Yoon
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  ABSTRACT: To find out the genetic factors of outbreak of thyroid gland disease, we developed the thyroid gland decision-making system, which processes the metabolic profile in steroid hormone map using a statistical method. Metabolic profile is a measured data of lots of mixed materials that includes not only known metabolites, but also unknown ones, which is estimated to have an influence on the thyroid gland disease. Therefore, to develop thyroid gland disease decision-making system, analyzing metabolic profile containing multi-materials would be useful for diagnosing thyroid gland disease. Because experimental values used for system construction are area values for the retention time, the observations are preprocessed through variable transition and t-test to use the area values concurrently and the highly correlated materials are estimated by principal component analysis. The thyroid gland decision-making system developed through the logistic regression is an excellent system demonstrating 98.7% accuracy in the classification table.
  Journal of Pharmaceutical and Biomedical Analysis 03/2007; 43(3):1100-5. · 2.97 Impact Factor
• Article: Screening test data analysis for liver disease prediction model using growth curve.

  Young Sun Kim, So Young Sohn, Dong Kee Kim, Donggeon Kim, Yong Han Paik, Ho Shik Shim
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  ABSTRACT: This study was done based on screening test data accumulated from 1994 to 2001 for studying of risk factor related with liver disease and prediction model of liver disease. In the existing study related with liver, the main current is studying on liver cancer, not on liver disease, previous step into liver cancer. As a result of estimating prediction model through the risk factors of liver disease and the growth curve on the basis of data, it is shown that most of the risk factors about liver disease are also those about known well as liver cancer. In addition, to investigate liver disease prevalence from the viewpoint of the future, this study presumed risk factor through the various growth curve analysis and examined logistic regression, decision tree and neural network from those estimators. In the case of neural network using growth curve estimator of Xi(5)=alphai+betaiT+epsiloniT, accuracy of liver disease was 72.55% and sensitivity was 78.62%. On the other hand, in the case of liver disease prediction model using recent screening test data estimator, accuracy was 72.09% and sensitivity was 71.72%. Those are lower than liver disease prediction model of growth curve analysis. In the various liver disease prediction models assumed by growth curve and many distinction models, when growth curve estimator was used, sensitivity value was improved.
  Biomedecine [?] Pharmacotherapy 01/2004; 57(10):482-8. · 2.00 Impact Factor