Abstract
A machine learning technique, decision tree, is used to predict the susceptibility to two liver diseases, chronic hepatitis and cirrhosis, from single nucleotide polymorphism(SNP) data. Also, it is used to identify a set of SNPs relevant to those diseases. The experimental results show that a decision tree is able to distinguish chronic hepatitis from normal with accuracy of 69.59% and cirrhosis from normal with accuracy of 76.72% and the C4.5 decision rule is with accuracy of 69.59% for chronic hepatitis and 79.31% for cirrhosis. The experimental results show that decision tree is a potential tool to predict the susceptibility to chronic hepatitis and cirrhosis from SNP data. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Kim, D. H., Uhmn, S., Ko, Y. W., Cho, S. W., Cheong, J. Y., & Kim, J. (2007). Chronic hepatitis and cirrhosis classification using SNP data, decision tree and decision rule. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4707 LNCS, pp. 585–596). Springer Verlag. https://doi.org/10.1007/978-3-540-74484-9_51
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