Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{197178,
        author = {Ms. Reshmi Pranabkumar Das and Prof. Tarun Yengatiwar},
        title = {Machine Learning-Based Predictive Analytics for Early Cervical Cancer Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5911-5921},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197178},
        abstract = {Cervical cancer still constitutes a major global health concern, particularly in underprivileged countries and middle-income nations where preventive measures and specialist diagnosis are not available. Clinically effective traditional screening methods such as Pap smear, HPV screening, and colposcopy have been found to be laborious, requiring huge amounts of resources and in some cases depending on the variability of humans. The recent advancements in machine learning (ML) and deep learning (DL) have supported the introduction of less than human errors data-driven solutions that could really enhance the early detection, diagnostic accuracy, and the overall process of screening. The paper that is under discussion here takes a very thorough look into the application of machine learning and artificial intelligence in cervical cancer detection, prognosis, and screening. Traditional ML algorithms, convolutional neural networks, ensemble models, and hybrid frameworks that use clinical, imaging, biomarker, and cytology datasets have all been systematically reviewed. Performance metrics, datasets, methodological strengths, and limitations have been critically dealt with in terms of comparison. The paper has highlighted the major challenges faced such as data imbalance, limited generalizability, lack of explainability, and barriers to real-world clinical deployment. The review places a strong emphasis on multimodal data integration, the establishment of standardized evaluation protocols, and the use of explainable AI in order to foster clinical trust and thus increase the deployment of AI in the health sector. In conclusion, the paper not only mentions the existing research trends in the field but also guides the future production of AI-based cervical cancer screening systems that are scalable, reliable, and clinically applicable.},
        keywords = {Cervical Cancer Detection, Machine Learning (ML), Predictive Modeling, Data Preprocessing, Healthcare etc.},
        month = {April},
        }