Vol. 3 No. 2 (2025): Issue - 02
Survey / Literature Reviews

Machine Learning Hybrid Models for Early Cervical Cancer Detection – A Comparative Study

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  • Geeta C Mara,
  • Syed Thouheed Ahmed,
  • Dollar Konjengbam Singh
Geeta C Mara
School of Computing and Information Technology, REVA University, Bangalore, India
Syed Thouheed Ahmed
School of Computing and Information Technology, REVA University, Bangalore, India
Dollar Konjengbam Singh
Department of ICT for Internet and Multimedia, University of Padua, Padua, Italy
DOI: https://doi.org/10.5281/zenodo.15555325

Published 2025-05-30

Keywords

  • AdaBoost,
  • Cervical Cancer,
  • Hybrid Models,
  • Stacking Classifier,
  • Logistic Regression Models

How to Cite

Geeta C Mara, Syed Thouheed Ahmed, & Dollar Konjengbam Singh. (2025). Machine Learning Hybrid Models for Early Cervical Cancer Detection – A Comparative Study. Milestone Transactions on Medical Technometrics, 3(2), 226–236. https://doi.org/10.5281/zenodo.15555325

Abstract

Cervical cancer arises as a result of the uncontrolled growth of abnormal cells in the cervix, usually caused by chronic infection with high-risk types of Human PapillomaVirus (HPV). Early detection and prevention can be facilitated through regular screening and HPV vaccination. This study proposes an enhanced ML Hybrid Model focused on the early spotting of Cervical Cancer using the best available Machine Learning techniques. To solve a primary challenge in diagnosing accuracy and precision in Cervical Cancer, the model utilizes AdaBoost, XGBoost, Stacking Classifiers, and Logistic Regression. The Hybrid Model uses ensemble methods such as AdaBoost and XGBoost, which improves productivity by properly integrating poor learners. Stacking Classifiers increases accuracy even more by incorporating the predictions of several models while Logistic Regression adds interpretability as well as reliability to the results. Collectively, these approaches form a model that produces low false positive rates alongside low false negative rates for early detection of the disease. This study focuses on the effects that Machine Learning can have in treating advanced stages of healthcare issues especially relating to early detection of Cervical cancer. The combination of sophisticated computational methods with clinical data sets presents an effective, globally relevant solution to urgent health concerns.

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