Open Access
Issue
ITM Web Conf.
Volume 86, 2026
5th International Conference on Current Research in Engineering and Technology (ICCRET-2026)
Article Number 01013
Number of page(s) 12
Section AI & Intelligent Computing
DOI https://doi.org/10.1051/itmconf/20268601013
Published online 05 June 2026
  1. M. Shanthini and A. G. Selvarani, “Evaluating Multiplex Biomarkers for Early Detection of Cervical Cancer,” pp. 881–886, Dec. 2024, doi: 10.1109/icicnis64247.2024.10823117. [Google Scholar]
  2. A. Yusuf, “Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions,” Apr. 2022, doi: 10.1101/2022.04.22.22274183. [Google Scholar]
  3. B. A. Leidel, “Deep Learning in Healthcare: Applications, Challenges, and Opportunities,” 2022, pp. 27–44. doi: 10.1007/978-981-19-2416-3_2. [Google Scholar]
  4. A. Yamijala, R. Visalakshi, and K. Srinivas, “Imbalanced data classification using improved synthetic minority over-sampling technique,” Multiagent and Grid Systems, vol. 19, pp. 117–131, Oct. 2023, doi: 10.3233/mgs-230007. [Google Scholar]
  5. S. Das, S. P. Nayak, B. Sahoo, and S. C. Nayak, “Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios,” pp. 774–779, Feb. 2024, doi: 10.1109/esic60604.2024.10481583. [Google Scholar]
  6. M. Hasan, P. Roy, and A. M. Nitu, “Cervical Cancer Classification using Machine Learning with Feature Importance and Model Explainability,” pp. 1–4, Dec. 2022, doi: 10.1109/ICECTE57896.2022.10114548. [Google Scholar]
  7. I. U. Sikder, Md. N. Hasan, R. Jahan, A. A. A. Mohamed, Nahida, and Y. Dirie, “A Comparative Study on Machine Learning Classifiers for Early Diagnosis of Cervical Cancer,” pp. 1–6, Sep. 2024, doi: 10.1109/compas60761.2024.10796200. [Google Scholar]
  8. K. Fernandes, J. S. Cardoso, and J. C. Fernandes, Cervical Cancer (Risk Factors) Dataset, UCI Machine Learning Repository, 2017. doi: 10.24432/C5Z310. [Google Scholar]
  9. T. M. Okyay, I. Yilmaz, and M. Koldas, “Evaluating Cervical Cancer Risk Using Machine Learning,” Medical Bulletin of Haseki, vol. 63, no. 4, pp. 188–194, 2025. doi: 10.4274/haseki.galenos.2025.35220. [Google Scholar]
  10. U. Yadav, V. D. Bondre, S. V. Bondre, B. Thakre, P. Agrawal, and S. Thakur, “Intelligent cervical cancer detection: empowering healthcare with machine learning algorithms,” IAES International Journal of Artificial Intelligence, vol. 14, no. 1, pp. 298–306, 2024. doi: http://doi.org/10.11591/ijai.v14.i1.pp298-306. [Google Scholar]
  11. N. Jiang et al., “Machine learning and deep learning to improve overall survival prediction in cervical cancer patients,” Translational Cancer Research, vol. 14, no. 5, pp. 3057–3068, 2025. [Google Scholar]
  12. S. Suryadi and M. Masrizal, “XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 9, no. 3, pp. 535–541, Jun. 2025, doi: 10.29207/resti.v9i3.6587. [Google Scholar]
  13. https://archive-eta.ics.uci.edu/dataset/383/cervical%2Bcancer%2Brisk%2Bfactors [Google Scholar]
  14. S. Kumar and V. Gota, “Logistic regression in cancer research: A narrative review of the concept, analysis, and interpretation,” Cancer research, statistics and treatment, vol. 6, pp. 573–578, Oct. 2023, doi: 10.4103/crst.crst_293_23. [Google Scholar]
  15. K.-L. Du, B. Jiang, J. Lu, J. Hua, and M. N. S. Swamy, “Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions,” Mathematics, vol. 12, no. 24, p. 3935, Dec. 2024, doi: 10.3390/math12243935. [Google Scholar]
  16. F. Curia, “Cervical cancer risk prediction with robust ensemble and explainable black boxes method,” Health technology, vol. 11, no. 4, pp. 875–885, May 2021, doi: 10.1007/S12553-021-00554-6. [Google Scholar]
  17. C. Che, H. Hu, X. Zhao, S. Li, and Q. Lin, “Advancing Cancer Document Classification with Random Forest,” Academic journal of science and technology, Nov. 2023, doi: 10.54097/ajst.v8i1.14333. [Google Scholar]

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