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.
This work was supported by a grant from the Korea Health 21 R&D Project, Ministry of Health and Welfare, Republic of Korea (A010383). Also, it was supported by Hallym University Research Fund, HRF-2004-40.
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Kim, DH., Uhmn, S., Ko, YW., Cho, S.W., Cheong, J.Y., Kim, J. (2007). Chronic Hepatitis and Cirrhosis Classification Using SNP Data, Decision Tree and Decision Rule. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_51
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