Open AccessMulticlass classification of acute lymphoblastic leukemia microarrays data using support vector machine algorithms
Author(s) -
. Hamidah,
Zuherman Rustam,
Surapong Utama,
Titin Siswantining
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 - 10.1088/1742-6596/1490/1/012027
Subject(s) - support vector machine , multiclass classification , acute leukemia , lymphoblastic leukemia , artificial intelligence , lymphoblast , leukemia , computer science , algorithm , machine learning , medicine , immunology , biology , genetics , cell culture
Acute lymphoblastic leukemia (ALL) is a form of leukemia, or cancer of the white blood cells characterized by excess lymphoblast. Classification of acute lymphoblastic leukemia subtypes based on fusion genes that have a translocation. The fusion genes are BCR-ABL, E2A-PBX1, Hyperdiploid > 50 chromosomes, MLL, T-ALL, and TEL-AML1. The classification of acute lymphoblastic leukemia subtypes has an important role for the type of treatment that will be received, duration of treatment, medication needed during treatment, and other treatments that may be needed. In this paper, the method used is Multiclass Support Vector Machine Recursive Feature Elimination (MSVM-RFE) as the feature selection and One-Against-One Multiclass Support Vector Machine (OAO-MSVM) with RBF-Kernel with σ = 0.01 and Polynomial-Kernel with d = 4 as the classification methods. For the multiclass classification of acute lymphoblastic leukemia microarrays data, the best method to use is the MSVM Polynomial-Kernel with d = 4 that produces overall accuracy about 94%, precision about 96%, recall about 95%, F1 score about 95%, and the running time is 0.66 seconds.