Detection of Hepatoma based on Gene Expression using Unitary Matrix of Singular Vector Decomposition

Abstract

Hepatoma is a long-term disease with a high risk of mortality. However, the disease is late detected, at the fourth level stadium due to silent symptoms. The infected hepatitis B virus gene HBx is a genome virus to trigger liver disease. This virus inserts material genetic into the host and disturbs the cell cycle. The regulation of gene expression is blocked to make work abnormal, especially for repairing and degrading. A microarray is a tool to quantify the RNA gene expression in huge volumes without any information for the related potential gene. Therefore, this study is proposed a feature extraction method using a unitary singular matrix for simplifying the classification model of hepatoma detection. Principally, the feature is decomposed using a singular vector to get the k-rank value of pattern. This matrix is applied to the representative machine learning algorithm, including KNN, Naive Bayes, C5.0 Decision Tree, and SVM. The experimental result achieved high performance with Area under the Curve (AUC) of above 90% on average.