An Explainable Artificial Intelligence Model for the Classification of Breast Cancer
Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized hea...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10229149/ |
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| author | Tarek Khater Abir Hussain Riyad Bendardaf Iman M. Talaat Hissam Tawfik Sam Ansari Soliman Mahmoud |
| author_facet | Tarek Khater Abir Hussain Riyad Bendardaf Iman M. Talaat Hissam Tawfik Sam Ansari Soliman Mahmoud |
| author_sort | Tarek Khater |
| collection | DOAJ |
| description | Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area’s worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease’s onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems. |
| format | Article |
| id | doaj-art-ee3ead12af1f43e8ae687f5fc45ae5ce |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ee3ead12af1f43e8ae687f5fc45ae5ce2025-01-14T00:02:13ZengIEEEIEEE Access2169-35362025-01-01135618563310.1109/ACCESS.2023.330844610229149An Explainable Artificial Intelligence Model for the Classification of Breast CancerTarek Khater0Abir Hussain1https://orcid.org/0000-0001-8413-0045Riyad Bendardaf2https://orcid.org/0000-0002-5158-4590Iman M. Talaat3Hissam Tawfik4Sam Ansari5https://orcid.org/0000-0003-3735-4972Soliman Mahmoud6https://orcid.org/0000-0003-0581-1796Electrical Engineering Department, University of Sharjah, Sharjah, United Arab EmiratesElectrical Engineering Department, University of Sharjah, Sharjah, United Arab EmiratesClinical Sciences Department, University of Sharjah, Sharjah, United Arab EmiratesClinical Sciences Department, University of Sharjah, Sharjah, United Arab EmiratesElectrical Engineering Department, University of Sharjah, Sharjah, United Arab EmiratesElectrical Engineering Department, University of Sharjah, Sharjah, United Arab EmiratesElectrical Engineering Department, University of Sharjah, Sharjah, United Arab EmiratesBreast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area’s worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease’s onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems.https://ieeexplore.ieee.org/document/10229149/Artificial intelligencebreast cancerexplainable machine learningmodel-agnosticpermutation importancepartial dependence plot |
| spellingShingle | Tarek Khater Abir Hussain Riyad Bendardaf Iman M. Talaat Hissam Tawfik Sam Ansari Soliman Mahmoud An Explainable Artificial Intelligence Model for the Classification of Breast Cancer IEEE Access Artificial intelligence breast cancer explainable machine learning model-agnostic permutation importance partial dependence plot |
| title | An Explainable Artificial Intelligence Model for the Classification of Breast Cancer |
| title_full | An Explainable Artificial Intelligence Model for the Classification of Breast Cancer |
| title_fullStr | An Explainable Artificial Intelligence Model for the Classification of Breast Cancer |
| title_full_unstemmed | An Explainable Artificial Intelligence Model for the Classification of Breast Cancer |
| title_short | An Explainable Artificial Intelligence Model for the Classification of Breast Cancer |
| title_sort | explainable artificial intelligence model for the classification of breast cancer |
| topic | Artificial intelligence breast cancer explainable machine learning model-agnostic permutation importance partial dependence plot |
| url | https://ieeexplore.ieee.org/document/10229149/ |
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