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.
| Published in | Machine Learning Research (Volume 5, Issue 2) |
| DOI | 10.11648/j.mlr.20200502.11 |
| Page(s) | 18-27 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Leukemia, Cancer, Feature Selection, Machine Learning, Classification
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APA Style
Md. Alamgir Sarder, Md. Maniruzzaman, Benojir Ahammed. (2020). Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques. Machine Learning Research, 5(2), 18-27. https://doi.org/10.11648/j.mlr.20200502.11
ACS Style
Md. Alamgir Sarder; Md. Maniruzzaman; Benojir Ahammed. Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques. Mach. Learn. Res. 2020, 5(2), 18-27. doi: 10.11648/j.mlr.20200502.11
AMA Style
Md. Alamgir Sarder, Md. Maniruzzaman, Benojir Ahammed. Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques. Mach Learn Res. 2020;5(2):18-27. doi: 10.11648/j.mlr.20200502.11
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Download@article{10.11648/j.mlr.20200502.11,
author = {Md. Alamgir Sarder and Md. Maniruzzaman and Benojir Ahammed},
title = {Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques},
journal = {Machine Learning Research},
volume = {5},
number = {2},
pages = {18-27},
doi = {10.11648/j.mlr.20200502.11},
url = {https://doi.org/10.11648/j.mlr.20200502.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20200502.11},
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.},
year = {2020}
}
TY - JOUR T1 - Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques AU - Md. Alamgir Sarder AU - Md. Maniruzzaman AU - Benojir Ahammed Y1 - 2020/07/04 PY - 2020 N1 - https://doi.org/10.11648/j.mlr.20200502.11 DO - 10.11648/j.mlr.20200502.11 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 18 EP - 27 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20200502.11 AB - 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. VL - 5 IS - 2 ER -
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