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BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a millio...

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Autores principales: Abbas, Shafaq, Jalil, Zunera, Javed, Abdul Rehman, Batool, Iqra, Khan, Mohammad Zubair, Noorwali, Abdulfattah, Gadekallu, Thippa Reddy, Akbar, Aqsa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959601/
https://www.ncbi.nlm.nih.gov/pubmed/33817036
http://dx.doi.org/10.7717/peerj-cs.390
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author Abbas, Shafaq
Jalil, Zunera
Javed, Abdul Rehman
Batool, Iqra
Khan, Mohammad Zubair
Noorwali, Abdulfattah
Gadekallu, Thippa Reddy
Akbar, Aqsa
author_facet Abbas, Shafaq
Jalil, Zunera
Javed, Abdul Rehman
Batool, Iqra
Khan, Mohammad Zubair
Noorwali, Abdulfattah
Gadekallu, Thippa Reddy
Akbar, Aqsa
author_sort Abbas, Shafaq
collection PubMed
description Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.
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spelling pubmed-79596012021-04-02 BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm Abbas, Shafaq Jalil, Zunera Javed, Abdul Rehman Batool, Iqra Khan, Mohammad Zubair Noorwali, Abdulfattah Gadekallu, Thippa Reddy Akbar, Aqsa PeerJ Comput Sci Bioinformatics Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy. PeerJ Inc. 2021-03-12 /pmc/articles/PMC7959601/ /pubmed/33817036 http://dx.doi.org/10.7717/peerj-cs.390 Text en © 2021 Abbas et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Abbas, Shafaq
Jalil, Zunera
Javed, Abdul Rehman
Batool, Iqra
Khan, Mohammad Zubair
Noorwali, Abdulfattah
Gadekallu, Thippa Reddy
Akbar, Aqsa
BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
title BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
title_full BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
title_fullStr BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
title_full_unstemmed BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
title_short BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
title_sort bcd-wert: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959601/
https://www.ncbi.nlm.nih.gov/pubmed/33817036
http://dx.doi.org/10.7717/peerj-cs.390
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