Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection

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

Text
21703-44306-1-PB.PDF
Download (516kB)

Official URL: https://ijeecs.iaescore.com/index.php/IJEECS/artic...

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Boruta, Breast cancer, LASSO, LR, Micrarray data, SVM
Divisions:	Faculty of Electrical Engineering > Department of Mechatronics Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Mar 2023 09:18
Last Modified:	06 Mar 2023 09:18
URI:	http://eprints.utem.edu.my/id/eprint/26132
Statistic Details:	View Download Statistic

Actions (login required)

View Item