Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection
Breast cancer is the most frequent cancer diagnosis amongst women worldwide. Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively conducted globally....
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Institute Of Advanced Engineering And Science (IAES)
2020
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my.utem.eprints.261322023-03-06T09:18:03Z http://eprints.utem.edu.my/id/eprint/26132/ Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection Mohd Ali, Nursabillilah Breast cancer is the most frequent cancer diagnosis amongst women worldwide. Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively conducted globally. The breast cancer classification is significantly important in ensuring reliable diagnostic system. Preliminary research on the usage of machine learning classifier and feature selection method for breast cancer classification is conducted here. Two feature selection methods namely Boruta and LASSO and SVM and LR classifier are studied. A breast cancer dataset from GEO web is adopted in this study. The findings show that LASSO with LR gives the best accuracy using this dataset. Institute Of Advanced Engineering And Science (IAES) 2020-11 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26132/2/21703-44306-1-PB.PDF Mohd Ali, Nursabillilah (2020) Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection. Indonesian Journal Of Electrical Engineering And Computer Science, 20 (2). pp. 712-719. ISSN 2502-4752 https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21703 10.11591/ijeecs.v20.i2.pp712-719 |
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Breast cancer is the most frequent cancer diagnosis amongst women worldwide. Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively conducted globally. The breast cancer classification is significantly important in ensuring reliable diagnostic system. Preliminary research on the usage of machine learning classifier and feature selection method for breast cancer
classification is conducted here. Two feature selection methods namely Boruta and LASSO and SVM and LR classifier are studied. A breast cancer dataset from GEO web is adopted in this study. The findings show that LASSO with LR gives the best accuracy using this dataset. |
| format |
Article |
| author |
Mohd Ali, Nursabillilah |
| spellingShingle |
Mohd Ali, Nursabillilah Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection |
| author_facet |
Mohd Ali, Nursabillilah |
| author_sort |
Mohd Ali, Nursabillilah |
| title |
Comparison of microarray breast cancer classification using
support vector machine and logistic regression with LASSO and boruta feature selection |
| title_short |
Comparison of microarray breast cancer classification using
support vector machine and logistic regression with LASSO and boruta feature selection |
| title_full |
Comparison of microarray breast cancer classification using
support vector machine and logistic regression with LASSO and boruta feature selection |
| title_fullStr |
Comparison of microarray breast cancer classification using
support vector machine and logistic regression with LASSO and boruta feature selection |
| title_full_unstemmed |
Comparison of microarray breast cancer classification using
support vector machine and logistic regression with LASSO and boruta feature selection |
| title_sort |
comparison of microarray breast cancer classification using
support vector machine and logistic regression with lasso and boruta feature selection |
| publisher |
Institute Of Advanced Engineering And Science (IAES) |
| publishDate |
2020 |
| url |
http://eprints.utem.edu.my/id/eprint/26132/2/21703-44306-1-PB.PDF http://eprints.utem.edu.my/id/eprint/26132/ https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21703 |
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13.188404 |