{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T07:01:49Z","timestamp":1772521309690,"version":"3.50.1"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"DOI":"10.1109\/sds68531.2025.11409730","type":"proceedings-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T20:53:31Z","timestamp":1772484811000},"page":"129-135","source":"Crossref","is-referenced-by-count":0,"title":["A Comparative Study of Machine Learning Models for Breast Cancer Diagnosis Using the Wisconsin Diagnostic Dataset"],"prefix":"10.1109","author":[{"given":"Masum","family":"Billah","sequence":"first","affiliation":[{"name":"University of Staffordshire,School of Digital, Technologies, Innovation and Business,Stoke,United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Hasan","sequence":"additional","affiliation":[{"name":"University of Staffordshire,School of Digital, Technologies, Innovation and Business,Stoke,United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abeda","family":"Raiyan","sequence":"additional","affiliation":[{"name":"Clinical Fellow The Christie NHS Foundation Trust,Manchester,United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Breast cancer fact sheet","journal-title":"World Health Organization"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref3","first-page":"104","article-title":"A comprehensive review of machine learning in medical imaging","volume":"139","author":"Saba","year":"2021","journal-title":"Computers in Biology and Medicine"},{"issue":"5","key":"ref4","first-page":"1012","article-title":"Nationwide real-world implementation of ai for cancer detection in mammography screening","volume":"30","author":"Conger","year":"2024","journal-title":"Nature Medicine"},{"key":"ref5","article-title":"Breast cancer wisconsin (diagnostic) data set","author":"Wolberg","year":"1995","journal-title":"UCI Machine Learning Repository"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2019.02.004"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2024.1444763"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"issue":"1","key":"ref10","first-page":"41","article-title":"So you\u2019ve got a high auc, now what? clinically relevant ml requires calibration and thresholding","volume":"8","author":"Deng","year":"2025","journal-title":"NPJ Digital Medicine"},{"key":"ref11","article-title":"Revisiting deep learning models for tabular data","author":"Gorishniy","year":"2021","journal-title":"Advances in Neural Information Processing Systems (NeurIPS)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.11.011"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"ref14","first-page":"109107","article-title":"Deep neural networks and tabular data: A survey","volume":"133","author":"Borisov","year":"2022","journal-title":"Pattern Recognition"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.01.009"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.04.224"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.29172\/7c2a6982-6d72-4cd8-bba6-2fccb06a7011"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2307\/2699986"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102430"},{"key":"ref21","article-title":"On calibration of modern neural networks","volume-title":"Proc. 34th Int. Conf. on Machine Learning (ICML)","author":"Guo"},{"key":"ref22","article-title":"Tabtransformer: Tabular data modeling using contextual embeddings","volume-title":"NeurIPS Workshop on Deep Learning for Tabular Data","author":"Huang"},{"key":"ref23","article-title":"Well-tuned simple nets excel on tabular datasets","volume-title":"Proceedings of the NeurIPS Workshop on Deep Learning for Tabular Data","author":"Kadra"},{"key":"ref24","first-page":"76532","article-title":"Efficient deep learning frameworks for tabular biomedical data: Balancing accuracy, efficiency, and interpretability","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"key":"ref25","article-title":"Energy-efficient early emergency detection for healthcare monitoring on wban platform","author":"Billah","year":"2021"}],"event":{"name":"2025 12th International Conference on Software Defined Systems (SDS)","location":"Lyon, France","start":{"date-parts":[[2025,12,2]]},"end":{"date-parts":[[2025,12,5]]}},"container-title":["2025 12th International Conference on Software Defined Systems (SDS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11409703\/11409692\/11409730.pdf?arnumber=11409730","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T06:26:39Z","timestamp":1772519199000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11409730\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/sds68531.2025.11409730","relation":{},"subject":[],"published":{"date-parts":[[2025,12,2]]}}}