<?xml version="1.0" encoding="utf-8"?>
<Journal>
<Journal-Info>
<name>International Journal of Pharma and Bio Sciences</name>
<website>ijpbs.net</website>
<email>editorijpbs@rediffmail.com (or) editorofijpbs@yahoo.com (or) prasmol@rediffmail.com</email>
</Journal-Info>
<article>
<article-id pub-id-type='other'>10.22376/ijpbs.2019.10.1.p1-12</article-id>
<issue_number>Volume 8 Issue 1</issue_number>
<issue_period>2017 (January - March)</issue_period>
<title><b>Analysis of Expression Level of BRCA Gene Using Machine Learning Algorithms for Diagnosis of Breast Cancer</b></title>
<abstract>The breast cancer is the second leading type of cancer which leads to death among women all over the world. Breast cancer exists due to the mutation occurs in the normal growth of BRCA gene under certain circumstances. In this paper, we used a novel approach for finding the disease – causing gene using computer-assisted algorithms. The existing algorithms are compared with each other to determine the efficiency in detecting the diseases from gene expression value. The results proved that the effectiveness of Hybrid Radial Bias Neural Network (HRBFNN) algorithm performs better than sequential and Divide and Conquer Kernel Solving Support Vector Machines (DCKSVM) algorithm in finding the diseased gene.</abstract>
<authors>J. SUMITHA AND T. DEVI</authors>
<keywords>DCKSVM, HRBFNN, Identification of diseased gene, Sequential model</keywords>
<pages>79-85</pages>
</article>
</Journal>
