An Analysis of Cervical Cancer using the Application of AI and Machine Learning

Abstract

Cervical cancer, a prevalent malignant neoplasm affecting the female reproductive system, is recognized globally as a prominent contributor to female mortality. Time-to-event analysis, essentially for all the clinical research, was found to be done by the survival prediction method very effectively. There is no screening and other preventive measures at hand and that is why cervical cancer is among the most urgent problems in a developing world. Cervical cancer will be covered in this article covering causes of its emergence, progression, symptoms, and its detection ways. It emphasizes the role played by machine learning in prediction and diagnosis of cervical cancer early, thus indicating the importance of preventive measures. Multiple machine learning algorithms including different approaches for cervical cancer prediction are studied which will include their pros and cons through an exhaustive literature analysis. Improved accuracy and clinical applicability should be the main objectives of this field, and this review helps to demonstrate the research gaps as well as the importance of integrating multiple data types, using a representative dataset, improving model understandability and implementing a holistic evaluation model. It is imperative that researchers fill the gaps in their models by collecting multi-modal data, using bigger and more relevant datasets and by designing models that are amenable to understanding, and creating reliable standards to appraise the outcomes. Moreover, the focus should be laid on the implementation and verification of predictive models in real-life clinical situations, so that they can assess their true value for cervical cancer prevention and patients’ results.