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Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net

Early diagnosis of breast cancer helps improve the patient's chance of survival. Therefore, cancer classification and feature selection are important research topics in medicine and biology. Recently, the adaptive elastic net was used effectively for feature-based cancer classification, allowin...

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Main Authors: Alharthi, Aiedh Mrisi, Lee, Muhammad Hisyam, Algama, Zakariya Yahya
Format: Article
Language:English
Published: Horizon Research Publishing 2021
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/95725/1/AiedhMrisi2021_ImprovingtheDiagnosisofBreastCancer.pdf
http://eprints.utm.my/id/eprint/95725/
http://dx.doi.org/10.13189/ujph.2021.090514
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spelling my.utm.957252022-05-31T13:18:15Z http://eprints.utm.my/id/eprint/95725/ Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net Alharthi, Aiedh Mrisi Lee, Muhammad Hisyam Algama, Zakariya Yahya QA Mathematics Early diagnosis of breast cancer helps improve the patient's chance of survival. Therefore, cancer classification and feature selection are important research topics in medicine and biology. Recently, the adaptive elastic net was used effectively for feature-based cancer classification, allowing simultaneous feature selection and feature coefficient estimation. The adaptive elastic net basically employed elastic net estimates as the initial weight. Nevertheless, the elastic net estimator is inconsistent and biased in selecting features. Therefore, the regularized logistic regression with the adaptive elastic net (RLRAEN) was used to handle the inconsistency problem by employing the adjusted variances of features as weights within the L1- regularization of the elastic net model. The proposed method was applied to the Wisconsin Breast Cancer dataset of the UCI repository and compared to the other existing penalized methods that were also applied to the same dataset. Based on the experimental study, the RLRAEN was more efficient in terms of feature selection and classification accuracy than the other competing methods. Therefore, it can be concluded that RLRAEN is a better method in breast cancer classification. Horizon Research Publishing 2021-10 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95725/1/AiedhMrisi2021_ImprovingtheDiagnosisofBreastCancer.pdf Alharthi, Aiedh Mrisi and Lee, Muhammad Hisyam and Algama, Zakariya Yahya (2021) Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net. Universal Journal of Public Health, 9 (5). pp. 317-323. ISSN 2331-8880 http://dx.doi.org/10.13189/ujph.2021.090514 DOI:10.13189/ujph.2021.090514
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Alharthi, Aiedh Mrisi
Lee, Muhammad Hisyam
Algama, Zakariya Yahya
Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
description Early diagnosis of breast cancer helps improve the patient's chance of survival. Therefore, cancer classification and feature selection are important research topics in medicine and biology. Recently, the adaptive elastic net was used effectively for feature-based cancer classification, allowing simultaneous feature selection and feature coefficient estimation. The adaptive elastic net basically employed elastic net estimates as the initial weight. Nevertheless, the elastic net estimator is inconsistent and biased in selecting features. Therefore, the regularized logistic regression with the adaptive elastic net (RLRAEN) was used to handle the inconsistency problem by employing the adjusted variances of features as weights within the L1- regularization of the elastic net model. The proposed method was applied to the Wisconsin Breast Cancer dataset of the UCI repository and compared to the other existing penalized methods that were also applied to the same dataset. Based on the experimental study, the RLRAEN was more efficient in terms of feature selection and classification accuracy than the other competing methods. Therefore, it can be concluded that RLRAEN is a better method in breast cancer classification.
format Article
author Alharthi, Aiedh Mrisi
Lee, Muhammad Hisyam
Algama, Zakariya Yahya
author_facet Alharthi, Aiedh Mrisi
Lee, Muhammad Hisyam
Algama, Zakariya Yahya
author_sort Alharthi, Aiedh Mrisi
title Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
title_short Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
title_full Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
title_fullStr Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
title_full_unstemmed Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
title_sort improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
publisher Horizon Research Publishing
publishDate 2021
url http://eprints.utm.my/id/eprint/95725/1/AiedhMrisi2021_ImprovingtheDiagnosisofBreastCancer.pdf
http://eprints.utm.my/id/eprint/95725/
http://dx.doi.org/10.13189/ujph.2021.090514
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score 13.211869

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