Volume 5, Issue 2, June 2020, Page: 18-27
Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques
Md. Alamgir Sarder, Statistics Discipline, Khulna University, Khulna, Bangladesh
Md. Maniruzzaman, Statistics Discipline, Khulna University, Khulna, Bangladesh
Benojir Ahammed, Statistics Discipline, Khulna University, Khulna, Bangladesh
Received: Feb. 26, 2020;       Accepted: Jun. 12, 2020;       Published: Jul. 4, 2020
DOI: 10.11648/j.mlr.20200502.11      View  173      Downloads  78
Abstract
Leukemia cancer is one of the most leading detrimental cancer diseases in worldwide. A huge number of genes are responsible for cancer diseases. Therefore, it is necessary to identify the most informative genes of Leukemia cancer. The main objectives of this study are to: (i) identify the most informative genes using five feature selection techniques (FST) and (ii) adopt six classifiers to classify the cancer disease and compare them. Leukemia cancer data has been taken from Kent ridge biomedical data repository, USA. There are 7129 genes and 72 patients. Among them, 47 patients are cancer and 25 are control. We have used five FST as t-test; Wilcoxon sign rank sum (WCSRS) test, random forest (RF), Boruta and least absolute shrinkage and selection operator (LASSO). We have also used six classifiers as Adaboost (AB), classification and regression tree (CART), artificial neural network (ANN), random forest (RF), linear discriminant analysis (LDA) and naive Bayes (NB). The performances of these classifiers are evaluated by accuracy (ACC), sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and F-measure (FM). We used simulated dataset to check the validity of proposed method. The results indicate that the combination of LASSO based FST and NB classifier gives the highest classification accuracy of 99.95%. On the basis of the results, we can conclude that the combination of LASSO based FST and NB classifier predicts the leukemia cancer more accurately compare to any other combination of FST and classifiers utilized in this study.
Keywords
Leukemia, Cancer, Feature Selection, Machine Learning, Classification
To cite this article
Md. Alamgir Sarder, Md. Maniruzzaman, Benojir Ahammed, Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques, Machine Learning Research. Vol. 5, No. 2, 2020, pp. 18-27. doi: 10.11648/j.mlr.20200502.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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