{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:59:57Z","timestamp":1776358797602,"version":"3.51.2"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum-Cent Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Breast cancer is a serious global health challenge, calling for the invention of a reliable and interpretable machine learning (ML) models for its early detection. The Wisconsin Breast Cancer (WBC), Wisconsin Diagnostic Breast Cancer (WDBC), and Coimbra datasets are the three publicly available datasets that were used to train the four-machine learning (ML) classification models which are compared in this study: Logistic Regression, Decision Trees, Random Forest, and CatBoost. These models\u2019 computational efficiency was measured by fit and test times, and their precision, recall, accuracy, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) were evaluated. To promote transparency for clinical adoption, feature-level analysis of the model\u2019s predictions was captured through the use of Local Interpretable Model-Agnostic Explanations (LIME). The highest accuracy was achieved using logistic regression, which recorded precision and recall values of 0.97\/0.95 (WDBC), 0.95\/0.92 (WBC), and 0.85\/0.78 (Coimbra), respectively, thereby exceeding other models in terms of efficiency and consistency. The Key factors that matched clinical expectations were identified by LIME, including BMI (Coimbra), clump thickness (WBC), and radius mean (WDBC). The present research builds on previous work by combining various datasets and interpretable methodologies to address ML black-box challenges in medical diagnostics. Future research should look into larger, multi-layered medical datasets and deep learning models to enhance classification accuracy.<\/jats:p>","DOI":"10.1007\/s44230-025-00111-8","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T15:04:40Z","timestamp":1757343880000},"page":"308-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Interpretable Machine Learning Approach for Breast Cancer Classification"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5518-6203","authenticated-orcid":false,"given":"Ahmad Tijjani","family":"Garba","sequence":"first","affiliation":[]},{"given":"Hafsah Shuaibu","family":"Hamza","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"111_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101550","author":"M Darwich","year":"2024","unstructured":"Darwich M, Bayoumi M. An evaluation of the effectiveness of machine learning prediction models in assessing breast cancer risk. Inf Med Unlocked. 2024. https:\/\/doi.org\/10.1016\/j.imu.2024.101550.","journal-title":"Inf Med Unlocked"},{"key":"111_CR2","doi-asserted-by":"publisher","DOI":"10.2196\/35750","author":"Y Gao","year":"2022","unstructured":"Gao Y, Li S, Jin Y, Zhou L, Sun S, Xu X, et al. An assessment of the predictive performance of current machine learning-based breast cancer risk prediction models: systematic review. JMIR Public Health Surveill. 2022. https:\/\/doi.org\/10.2196\/35750.","journal-title":"JMIR Public Health Surveill"},{"key":"111_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.596364","author":"JY Kim","year":"2021","unstructured":"Kim JY, Lee YS, Yu J, Park Y, Lee SK, Lee M, et al. Deep learning-based prediction model for breast cancer recurrence using adjuvant breast cancer cohort in tertiary cancer center registry. Front Oncol. 2021. https:\/\/doi.org\/10.3389\/fonc.2021.596364.","journal-title":"Front Oncol"},{"key":"111_CR4","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.procs.2018.05.197","volume":"132","author":"M Kumari","year":"2018","unstructured":"Kumari M, Singh V. Breast cancer prediction system. Procedia Comput Sci. 2018;132:371\u20136. https:\/\/doi.org\/10.1016\/j.procs.2018.05.197.","journal-title":"Procedia Comput Sci"},{"key":"111_CR5","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.procs.2021.07.062","volume":"191","author":"MA Naji","year":"2021","unstructured":"Naji MA, Filali SE, Aarika K, Benlahmar EH, Abdelouhahid RA, Debauche O. Machine learning algorithms for breast cancer prediction and diagnosis. Procedia Comput Sci. 2021;191:487\u201392. https:\/\/doi.org\/10.1016\/j.procs.2021.07.062.","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"111_CR6","doi-asserted-by":"publisher","first-page":"238","DOI":"10.54254\/2753-8818\/32\/20240871","volume":"32","author":"W Shengjie","year":"2024","unstructured":"Shengjie W. Design and implementation of breast cancer prediction system based on machine learning. Theor Nat Sci. 2024;32(1):238\u201347. https:\/\/doi.org\/10.54254\/2753-8818\/32\/20240871.","journal-title":"Theor Nat Sci"},{"issue":"08","key":"111_CR7","doi-asserted-by":"publisher","first-page":"348","DOI":"10.4236\/jsea.2023.168018","volume":"16","author":"Y Wei","year":"2023","unstructured":"Wei Y, Zhang D, Gao M, Tian Y, He Y, Huang B, et al. Breast cancer prediction based on machine learning. J Softw Eng Appl. 2023;16(08):348\u201360. https:\/\/doi.org\/10.4236\/jsea.2023.168018.","journal-title":"J Softw Eng Appl"},{"key":"111_CR8","doi-asserted-by":"publisher","unstructured":"Ara S, Das A, Dey A. Malignant and benign breast cancer classification using machine learning algorithms. In 2021 International Conference on Artificial Intelligence (ICAI). IEEE. 2021. p. 97\u2013102. https:\/\/doi.org\/10.1109\/ICAI52203.2021.9445249","DOI":"10.1109\/ICAI52203.2021.9445249"},{"issue":"10","key":"111_CR9","doi-asserted-by":"publisher","first-page":"5025","DOI":"10.1109\/JBHI.2022.3187765","volume":"26","author":"M Liu","year":"2022","unstructured":"Liu M, Hu L, Tang Y, Wang C, He Y, Zeng C, et al. A deep learning method for breast cancer classification in the pathology images. IEEE J Biomed Health Inform. 2022;26(10):5025\u201332. https:\/\/doi.org\/10.1109\/JBHI.2022.3187765.","journal-title":"IEEE J Biomed Health Inform"},{"key":"111_CR10","doi-asserted-by":"publisher","first-page":"184119","DOI":"10.1109\/ACCESS.2024.3503413","volume":"12","author":"M Hayat","year":"2024","unstructured":"Hayat M, Ahmad N, Nasir A, Tariq ZA. Hybrid deep learning EfficientNetV2 and vision transformer (EffNetV2-ViT) model for breast cancer histopathological image classification. IEEE Access. 2024;12:184119\u201331. https:\/\/doi.org\/10.1109\/ACCESS.2024.3503413.","journal-title":"IEEE Access"},{"key":"111_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2023.100126","volume":"9","author":"A Karuppasamy","year":"2024","unstructured":"Karuppasamy A, et al. Feed-forward networks using logistic regression and support vector machine for whole-slide breast cancer histopathology image classification. 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In Proceedings of the ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). arXiv. https:\/\/arxiv.org\/abs\/1606.05386"}],"container-title":["Human-Centric Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00111-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44230-025-00111-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44230-025-00111-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T10:22:57Z","timestamp":1758277377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44230-025-00111-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":18,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["111"],"URL":"https:\/\/doi.org\/10.1007\/s44230-025-00111-8","relation":{},"ISSN":["2667-1336"],"issn-type":[{"value":"2667-1336","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,8]]},"assertion":[{"value":"7 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We (authors) hereby declare that there are no competing interests, either financial or non-financial, directly or indirectly related to the work submitted for publication. We also declare that no funding was received for the completion of this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable. This manuscript does not contain any individual person\u2019s data in any form (e.g., individual details, images, or videos).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"This study utilized publicly available datasets (WDBC and Coimbra datasets) containing anonymized human data for breast cancer classification. As no primary data collection involving human participants, human tissue, or animals was conducted, ethics approval and consent to participate were not required. The use of these datasets complies with the ethical guidelines outlined by their respective sources. No studies involving plants were conducted in this research.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}