2007 Frontiers in the Convergence of Bioscience and Information Technologies Chronic Hepatitis Classification Using SNP Data and Data Mining Techniques Jeju Island, Korea October 11-October 13 ISBN: 978-0-7695-2999-8
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FBIT.2007.64
The machine learning techniques, SVM, decision tree, and decision rule, are used to predict the susceptibility to the liver disease, chronic hepatitis from single nucleotide polymorphism(SNP) data. Also, they are used to identify a set of SNPs relevant to the disease. In addition, we apply backtracking technique to couple of feature selection algorithms, forward selection and backward elimination, and show that this technique is beneficial to find the better solutions by experiments. The experimental results show that decision rule is able to distinguish chronic hepatitis from normal with the maximum accuracy of 73.20%, whereas SVM is with 67.53% and decision tree is with 72.68%. It is also shown that decision tree and decision rule are potential tools to predict the susceptibility to chronic hepatitis from SNP data.
Citation:
Saangyong Uhmn, Dong-Hoi Kim, Sung Won Cho, Jae Youn Cheong, Jin Kim, "Chronic Hepatitis Classification Using SNP Data and Data Mining Techniques," fbit, pp.81-86, 2007 Frontiers in the Convergence of Bioscience and Information Technologies, 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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