Chronic hepatitis and cirrhosis classification using SNP data, decision tree and decision rule

Authors:
Dong-Hoi Kim;Saangyong Uhmn;Young-Woong Ko;Sung Won Cho;Jae Youn Cheong;Jin Kim
Affiliations:
Department of Computer Engineering, Hallym University, Chuncheon, Kangwondo, Republic of Korea;Department of Computer Engineering, Hallym University, Chuncheon, Kangwondo, Republic of Korea;Department of Computer Engineering, Hallym University, Chuncheon, Kangwondo, Republic of Korea;Genomic Research Center for Gastroenterology, School of Medicine, Ajou University, Suwon, Gyeonggi-do, Republic of Korea;Genomic Research Center for Gastroenterology, School of Medicine, Ajou University, Suwon, Gyeonggi-do, Republic of Korea;Department of Computer Engineering, Hallym University, Chuncheon, Kangwondo, Republic of Korea
Venue:
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
Year:
2007

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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.