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

Kim, DH; Uhmn, S; Ko, YW; Cho, SW; Cheong, JY; Kim, J
Lecture notes in computer science, 4707(3):585-596, 2007
Journal Title
Lecture notes in computer science
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.
Liver diseasesDecision treesLearning systemsNucleotidesPolymorphism
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Gastroenterology
AJOU Authors
조, 성원정, 재연
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