Machine Learning Approaches in Cervical Cancer Research: A Comprehensive Literature Review
Murathan Yüksel, Turgut Özseven · 2025
Cervical cancer (CrC) continues to pose a significant global threat to women’s health and accounts for a considerable share of the worldwide cancer burden. Early diagnosis and effective management strategies play a critical role in reducing the morbidity and mortality rates associated with the disease. This review aims to comprehensively evaluate the epidemiology, etiology, traditional screening and diagnostic methods, and the current and potential applications of machine learning (ML) and artificial intelligence (AI) algorithms in cervical cancer. The reviewed studies cover a broad spectrum, ranging from the role of Human Papillomavirus (HPV) infection in disease progression to cancer cell classification, prediction of treatment response, and individual risk assessment. This review highlights the transformative potential of ML and AI techniques in the fight against cervical cancer, demonstrating how advanced methods such as feature selection, ensemble learning, and explainable artificial intelligence (XAI) enhance diagnostic accuracy and clinical applicability.