Open AccessThe visualization and classification method of support vector machine in lymphoma cancer
Author(s) -
B. C. Kristina,
Alfian Futuhul Hadi,
Abduh Riski,
Ahmad Kamsyakawuni,
Dian Anggraeni
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1613/1/012065
Subject(s) - support vector machine , artificial intelligence , computer science , visualization , machine learning , follicular lymphoma , pattern recognition (psychology) , polynomial kernel , kernel (algebra) , data mining , kernel method , mathematics , lymphoma , medicine , combinatorics , immunology
In the classical-classification multivariate process, it becomes an interesting topic to be discussed in the research area because of the larger variables with smaller observations. For this we need a method that can handle this problem. One answer is to use machine learning. SVM is a classification method in machine learning that is able to classify these data types. In addition, SVM can also model and classify relationships between variables efficiently and easy interpretation. This paper aims to create a visualization of SVM classifiers, then obtain an accuracy value to have an optimal classification with a misclassification of small numbers. This study aims to find good SVM input parameters by assessing the importance of variables using visual methods. This visualization will distinguish groups of people who contract diffuse lymphoma cancer and follicular lymphoma cancer with data on the genetic expression of lymphoma cancer. The classification using kernel Linear, Gaussian RBF, Polynomial and Sigmoid. The best classification accuracy using linear kernel functions with training data has a classification accuracy of 100% and testing data has a classification accuracy of 94, 73%.