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Cervical Cancer Prediction Empowered with Federated Machine Learning

Muhammad Umar Nasir1, Omar Kassem Khalil2, Karamath Ateeq3, Bassam SaleemAllah Almogadwy4, M. A. Khan5, Khan Muhammad Adnan6,*

1 Department of Computer Sciences, Bahria University Lahore Campus, Lahore, 54000, Pakistan
2 Faculty of Information Technology, Liwa College, Abu Dhabi, 20009, UAE
3 Department of Computing, Skyline University College, Sharjah, 999041, UAE
4 Department of Computer Science, Taibah University, Medina, 42315, Saudi Arabia
5 Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore, 54000, Pakistan
6 Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam, 13120, Korea

* Corresponding Author: Khan Muhammad Adnan. Email: email

Computers, Materials & Continua 2024, 79(1), 963-981. https://doi.org/10.32604/cmc.2024.047874

Abstract

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of Medical Things (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardous attacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74% misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stages with the help of patient clinical records, and all medical professionals will get beneficial diagnosing approaches from this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due to cervical cancer.

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Cite This Article

APA Style
Nasir, M.U., Khalil, O.K., Ateeq, K., Almogadwy, B.S., Khan, M.A. et al. (2024). Cervical cancer prediction empowered with federated machine learning. Computers, Materials & Continua, 79(1), 963-981. https://doi.org/10.32604/cmc.2024.047874
Vancouver Style
Nasir MU, Khalil OK, Ateeq K, Almogadwy BS, Khan MA, Adnan KM. Cervical cancer prediction empowered with federated machine learning. Comput Mater Contin. 2024;79(1):963-981 https://doi.org/10.32604/cmc.2024.047874
IEEE Style
M.U. Nasir, O.K. Khalil, K. Ateeq, B.S. Almogadwy, M.A. Khan, and K.M. Adnan "Cervical Cancer Prediction Empowered with Federated Machine Learning," Comput. Mater. Contin., vol. 79, no. 1, pp. 963-981. 2024. https://doi.org/10.32604/cmc.2024.047874



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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