A study on application of single nucleotide polymorphism and machine learning techniques to diagnosis of chronic hepatitis.

Abstract

We used several machine learning techniques to predict the susceptibility to chronic hepatitis from single nucleotide polymorphism (SNP) data. These are integrated with several feature selection algorithms to identify a set of SNPs relevant to the disease. In addition, we apply a backtracking technique to a couple of feature selection algorithms, forward selection and backward elimination, and show that it is beneficial to find the better solutions by experiment. The experimental results show that the decision rule is able to distinguish chronic hepatitis from normal with a maximum accuracy of 73.20%, whereas the accuracy of the support vector machine is 67.53% and that of the decision tree is 72.68%. It is also shown that the decision tree and decision rule are potential tools to predict susceptibility to chronic hepatitis from SNP data.