Early Detection of Cervical Cancer Through Data-Driven Machine Learning Approaches: A Comprehensive Review

Abstract

Cervical cancer is a leading cause of mortality among women worldwide, particularly in developing countries where access to regular screening and timely diagnosis is limited. Early detection is critical to improve patient outcomes, yet conventional diagnostic methods such as Pap smears and colposcopy are time-consuming, costly, and prone to human error. This study proposes a machine learning-based predictive system for early detection of cervical cancer using patient demographic data, medical history, and laboratory test results. The system integrates data preprocessing, feature extraction, and selection techniques to handle missing values, noise, and class imbalance. Multiple supervised learning algorithms—including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, Random Forest, Gradient Boosting, AdaBoost, and XGBoost—are developed and evaluated using metrics such as accuracy, precision, recall, and F1-score. The platform is implemented in Python, providing a user-friendly decision support interface for healthcare professionals. By automating the prediction process and improving diagnostic accuracy, this system aims to facilitate early intervention, reduce clinical workload, and contribute to lowering the global burden of cervical cancer, particularly in resource-constrained healthcare settings.

Introduction

Cervical cancer is a leading cause of cancer-related deaths among women worldwide, particularly in developing countries where limited access to screening and healthcare results in late-stage diagnosis. Early detection is critical for reducing mortality, yet traditional diagnostic methods such as Pap smears, colposcopy, and biopsy are time-consuming, prone to human error, and often inaccessible in low-resource settings.

Recent advancements in machine learning (ML) and artificial intelligence (AI) have shown significant potential in improving cervical cancer detection by analyzing complex medical data to identify at-risk individuals. Various supervised learning algorithms—including Logistic Regression, Decision Trees, SVM, Random Forest, Gradient Boosting, AdaBoost, and XGBoost—have achieved high accuracy in predictive modeling. However, challenges such as data imbalance, missing values, and limited dataset sizes hinder model generalization. To address these, preprocessing techniques like normalization, feature selection, and SMOTE oversampling are applied to enhance data quality and model performance.

Conclusion

This review highlights the transformative potential of machine learning (ML) and deep learning (DL) in improving the early detection, diagnosis, and prognosis of cervical cancer. The reviewed studies collectively demonstrate that AI-based models can achieve high accuracy, sensitivity, and specificity, surpassing traditional diagnostic techniques such as Pap smears and colposcopy. However, despite these promising advancements, several challenges remain, including data imbalance, lack of standardized datasets, model interpretability, and limited clinical validation. Effective preprocessing, robust feature selection, and explainable AI frameworks are essential for ensuring model reliability and acceptance in clinical settings. Future work should focus on integrating multi-modal datasets, conducting large-scale clinical trials, and developing user-friendly diagnostic tools that can be implemented in real-world healthcare systems, especially in resource-limited regions. Ultimately, the adoption of AI-driven cervical cancer screening systems holds great potential to enhance diagnostic accuracy, enable personalized treatment, and reduce mortality rates globally.

References

[1] B. Vázquez, M. Rojas-García, J. I. Rodríguez-Esquivel, et al., “Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review,” Diagnostics, vol. 15, no. 12, p. 1543, 2025. [2] M. Fang, B. Liao, X. Lei, and F.-X. Wu, “A systematic review on deep learning based methods for cervical cell image analysis,” Sci. Direct, 2024. [3] P. Xue, L. Dang, L.-H. Kong, H.-P. Tang, H.-M. Xu, and H.-Y. Weng, “Deep learning enabled liquid-based cytology model for cervical screening or triage,” Nat. Commun., 2025. [4] L. Liu, J. Liu, Q. Su, Y. Chu, H. Xia, and R. Xue, “Performance of artificial intelligence for diagnosing cervical cytology and colposcopy: systematic review and meta-analysis,” eClinicalMedicine, The Lancet, 2024. [5] R. Baber, N. Latif, K. Raza, U. Zaman, F. Hafsa, and F. Faiza, “Integrating Machine Learning with Pap Smear and HPV Screening,” J. Neonatal Surgery, 2024/2025. [6] L. She, Y. Li, H. Wang, and J. Zhang, “Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis,” J. Med. Internet Res., vol. 27, p. e71091, 2025. [7] Y. Zhao, J. Cui, and L. Qiu, “Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis,” eClinicalMedicine, 2025. [8] E. E. Onuiri, C. Ogbonna, and K. C. Umeaka, “Performance of Predictive Models in Cervical Cancer Recurrence: A Systematic Review and Meta-Analysis of Biomarkers and Prognosis,” Asian J. Comput. Sci. Technol., vol. 13, no. 2, 2024. [9] Q. Wen, S. Wang, Y. Min, X. Liu, J. Fang, J. Lang, and M. Chen, “Associations of the gut, cervical, and vaginal microbiota with cervical cancer: a systematic review and meta-analysis,” BMC Women’s Health, vol. 25, p. 65, 2025. [10] C.-Q. Jiang, X.-J. Li, Z.-Y. Zhou, Q. Xin, and L. Yu, “Image-based AI models in detecting lymph node metastasis (LNM) in cervical cancer patients: Systematic Review and Meta-Analysis,” Front. Oncol., 2025.

Copyright

Copyright © 2025 Ms. Dnyaneshwari P. Badhekar, Prof. Rashmi Kannake. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.