Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm
Over the past decade, breast cancer has been the most common type of cancer in women. Different methods were proposed for breast cancer detection. These methods mainly classify and categorize malignant and Benign tumors. Machine learning is a practical approach for breast cancer classification. Data...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10410858/ |
| _version_ | 1827369199070085120 |
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| author | Amreen Batool Yung-Cheol Byun |
| author_facet | Amreen Batool Yung-Cheol Byun |
| author_sort | Amreen Batool |
| collection | DOAJ |
| description | Over the past decade, breast cancer has been the most common type of cancer in women. Different methods were proposed for breast cancer detection. These methods mainly classify and categorize malignant and Benign tumors. Machine learning is a practical approach for breast cancer classification. Data mining and classification are effective methods to predict and categorize breast cancer. The optimum classification for detecting Breast Cancer (BC) is ensemble-based. The ensemble approach involves using multiple ways to find the best possible solution. This study used the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. We created a voting ensemble classifier that combines four different machine learning models: Extra Trees Classifier (ETC), Light Gradient Boosting Machine (LightGBM), Ridge Classifier (RC), and Linear Discriminant Analysis (LDA). The proposed ELRL-E approach achieved an accuracy of 97.6%, a precision of 96.4%, a recall of 100%, and an F1 score of 98.1%. Various output evaluations are used to evaluate the performance and efficiency of the proposed model and other classifiers. Overall, the recommended strategy performed better. Results are directly compared with the individual classifier and different recognized state-of-the-art classifiers. The primary objective of this study is to identify the most influential ensemble machine learning classifier for breast cancer detection and diagnosis in terms of accuracy and AUC score. |
| first_indexed | 2024-03-08T09:42:45Z |
| format | Article |
| id | doaj.art-6da94575df4c4f679fa7f680ded90948 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T09:42:45Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6da94575df4c4f679fa7f680ded909482024-01-30T00:03:45ZengIEEEIEEE Access2169-35362024-01-0112128691288210.1109/ACCESS.2024.335660210410858Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning AlgorithmAmreen Batool0https://orcid.org/0000-0002-6041-653XYung-Cheol Byun1https://orcid.org/0000-0003-1107-9941Department of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju-si, South KoreaDepartment of Computer Engineering, Major of Electronic Engineering, Institute of Information Science and Technology, Jeju National University, Jeju-si, South KoreaOver the past decade, breast cancer has been the most common type of cancer in women. Different methods were proposed for breast cancer detection. These methods mainly classify and categorize malignant and Benign tumors. Machine learning is a practical approach for breast cancer classification. Data mining and classification are effective methods to predict and categorize breast cancer. The optimum classification for detecting Breast Cancer (BC) is ensemble-based. The ensemble approach involves using multiple ways to find the best possible solution. This study used the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. We created a voting ensemble classifier that combines four different machine learning models: Extra Trees Classifier (ETC), Light Gradient Boosting Machine (LightGBM), Ridge Classifier (RC), and Linear Discriminant Analysis (LDA). The proposed ELRL-E approach achieved an accuracy of 97.6%, a precision of 96.4%, a recall of 100%, and an F1 score of 98.1%. Various output evaluations are used to evaluate the performance and efficiency of the proposed model and other classifiers. Overall, the recommended strategy performed better. Results are directly compared with the individual classifier and different recognized state-of-the-art classifiers. The primary objective of this study is to identify the most influential ensemble machine learning classifier for breast cancer detection and diagnosis in terms of accuracy and AUC score.https://ieeexplore.ieee.org/document/10410858/Breast cancerclassificationmachine learningvoting classifierensemble learning |
| spellingShingle | Amreen Batool Yung-Cheol Byun Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm IEEE Access Breast cancer classification machine learning voting classifier ensemble learning |
| title | Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm |
| title_full | Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm |
| title_fullStr | Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm |
| title_full_unstemmed | Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm |
| title_short | Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm |
| title_sort | toward improving breast cancer classification using an adaptive voting ensemble learning algorithm |
| topic | Breast cancer classification machine learning voting classifier ensemble learning |
| url | https://ieeexplore.ieee.org/document/10410858/ |
| work_keys_str_mv | AT amreenbatool towardimprovingbreastcancerclassificationusinganadaptivevotingensemblelearningalgorithm AT yungcheolbyun towardimprovingbreastcancerclassificationusinganadaptivevotingensemblelearningalgorithm |