Multiclass classification of acute lymphoblastic leukemia microarrays data using support vector machine algorithms

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Abstract

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 s = 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.

Original languageEnglish
Article number012027
JournalJournal of Physics: Conference Series
Volume1490
Issue number1
DOIs
Publication statusPublished - 9 Jun 2020
Event5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019 - Surabaya, Indonesia
Duration: 19 Oct 2019 → …

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