Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Review On Predictive Analytics for Cervical Cancer: A Machine Learning-Based Early Detection Framework

Author(s) Diksha Chaudhary, Dr. Gurudev Sawarkar, Prof. Harshali Ragite
Country India
Abstract Cervical cancer is still one of the main global health problems, especially in poor and middle-income countries that donot have early screening facilities and specialist diagnosis. Traditional methods of screening like Pap smear, HPV screening,and colposcopy, even though they are clinically effective, have the drawbacks of being tedious, requiring lots of resources,and being dependent on human variability. The recent developments in machine learning (ML) and deep learning (DL) havepaved the way for the introduction of data-driven solutions that can significantly improve the early detection, diagnosticaccuracy, and screening efficiency. The review paper under discussion analyses in depth the application of machine learningand artificial intelligence in cervical cancer detection, prognosis, and screening. Traditional ML algorithms, convolutionalneural networks, ensemble models, and hybrid frameworks using clinical, imaging, biomarker, and cytology datasets havebeen systematically reviewed. Performance metrics, datasets, methodological strengths, and limitations have been criticallycompared. Major challenges such as data imbalance, limited generalizability, lack of explainability, and barriers to real-worldclinical deployment have been pointed out. The review stresses the necessity of multimodal data integration, standardizedevaluation protocols, and explainable AI to build clinical trust and increase the use of AI in the healthcare sector. To sum up,the paper not only tells about the existing research trends in the field but also gives directions for the future development ofAI-based cervical cancer screening systems that are scalable, reliable, and clinically applicable.
Keywords Cervical Cancer, Machine Learning, Deep Learning, Early Detection, Medical Decision Support Systems
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-27
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5761
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