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Peer-Reviewed Publication
Sci Rep2026;16(1)March 5, 2026Journal Article

Enhanced cervical cancer diagnosis using a novel Bayesian fusion ensemble method with explainable AI.

Oahidul Islam1, Md Assaduzzaman2,3, Sumaia Akter4, Nafiz Fahad5,6, Md Jakir Hossen7,8
1Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
2Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
3DeepHealth Research Lab, Dhaka, Bangladesh.
4Department of Software Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
5Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia.
6Elite Research Lab, 17010 Cedarcroft Rd, Queens, NY, 11432, USA.
7Centre for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering Technology (FET), Multimedia University, 75450, Melaka, Malaysia. jakir.hossen@mmu.edu.my.
8Elite Research Lab, 17010 Cedarcroft Rd, Queens, NY, 11432, USA. jakir.hossen@mmu.edu.my.
PubMed PMC DOI PDF

Abstract

Cervical cancer is one of the leading causes of death in women, especially in low- and middle-income countries. Early disease detection is crucial for improving survival, but conventional methods are inefficient, costly, and error-prone. In this study, we present a hybrid machine learning framework that diagnoses cervical cancer risk from clinical and behavioral records by analyzing 36 patient att…

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