In Silico Analysis Using Hybrid Support Vector Machine and Second Order of Markov Chain for Multiple Sequence Alignment to Identify the Types of Leukaemia

Mohammad Isa Irawan, Mohammad Hamim Zajuli Al Faroby, Awik Pudji Dyah Nurhayati

Research output: Contribution to journalConference articlepeer-review

Abstract

Cancer is a disease that causes an abnormal growth of cells and can attack every part of the body, which is occurred because of a damage in deoxyribonucleic acid (DNA) that leads to a mutation in a vital gene that controls cell division. The biomarker technology that used in clinical practice still used a high cost and need a long time to detect the cancer signs. As the former studies about cancer, the biomarker has been detected in the microarray data. In this paper, we used a support vector machine (SVM) to classify 4 type of leukaemia. Begin with extracting the data feature of sequence DNA from a string into numeric using Second order of Markov chain, SVM classified DNA using 40 data for the training step and 25 data for testing step. In this paper, SVM used 3 types of the kernel, which are linear, Gaussian radial basis function, and polynomial. The results showed that the Gaussian kernel has the best accuracy then other kernel.

Original languageEnglish
Article number012052
JournalJournal of Physics: Conference Series
Volume1366
Issue number1
DOIs
Publication statusPublished - 7 Nov 2019
Event2nd International Conference on Applied and Industrial Mathematics and Statistics 2019, ICoAIMS 2019 - Kuantan, Pahang, Malaysia
Duration: 23 Jul 201925 Jul 2019

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