Abstract: In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75%.

Mohammed H. Afif, Abdel-Rahman Hedar, Taysir H. Abdel Hamid and Yousef B. Mahdy, “SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 4(2), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040208

@article{Afif2013,
title = {SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040208},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040208},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {2},
author = {Mohammed H. Afif and Abdel-Rahman Hedar and Taysir H. Abdel Hamid and Yousef B. Mahdy}
}