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

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.