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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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-03-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-390.pdf |
| _version_ | 1818677165622820864 |
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| author | Shafaq Abbas Zunera Jalil Abdul Rehman Javed Iqra Batool Mohammad Zubair Khan Abdulfattah Noorwali Thippa Reddy Gadekallu Aqsa Akbar |
| author_facet | Shafaq Abbas Zunera Jalil Abdul Rehman Javed Iqra Batool Mohammad Zubair Khan Abdulfattah Noorwali Thippa Reddy Gadekallu Aqsa Akbar |
| author_sort | Shafaq Abbas |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T08:55:02Z |
| format | Article |
| id | doaj.art-a81af271bc464750a2ff5182a3a9b828 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-17T08:55:02Z |
| publishDate | 2021-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-a81af271bc464750a2ff5182a3a9b8282022-12-21T21:55:59ZengPeerJ Inc.PeerJ Computer Science2376-59922021-03-017e39010.7717/peerj-cs.390BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithmShafaq Abbas0Zunera Jalil1Abdul Rehman Javed2Iqra Batool3Mohammad Zubair Khan4Abdulfattah Noorwali5Thippa Reddy Gadekallu6Aqsa Akbar7Department of Computer Science, Air University, Islamabad, PakistanDepartment of Cyber Security, Air University, Islamabad, PakistanDepartment of Cyber Security, Air University, Islamabad, PakistanDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi ArabiaElectrical Engineering Department, Umm Al-Qura University, Makkah, Saudi ArabiaSchool of Information Technology and Engineering, Vellore Institute of Technology University, Tamil Nadu, IndiaDepartment of Computer Science, Air University, Islamabad, PakistanBreast 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.https://peerj.com/articles/cs-390.pdfBreast cancerMachine learningWhale optimization algorithmSupport vector machine |
| spellingShingle | Shafaq Abbas Zunera Jalil Abdul Rehman Javed Iqra Batool Mohammad Zubair Khan Abdulfattah Noorwali Thippa Reddy Gadekallu Aqsa Akbar BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm PeerJ Computer Science Breast cancer Machine learning Whale optimization algorithm Support vector machine |
| 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 | Breast cancer Machine learning Whale optimization algorithm Support vector machine |
| url | https://peerj.com/articles/cs-390.pdf |
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