THE USE OF MACHINE LEARNING IN THE EARLY DETECTION OF CERVICAL CANCER
DOI:
https://doi.org/10.5281/zenodo.20681130Keywords:
Machine Learning, Cervical Cancer, Early Diagnosis, Gynecological NursingAbstract
Abstract: Introduction: Cervical cancer is one of the leading causes of morbidity and mortality among women worldwide, accounting for approximately 604,000 new cases and 342,000 deaths annually, according to estimates from the World Health Organization. Objective: To analyze, through an integrative literature review, the state of the art and the effectiveness of machine learning algorithms applied to the early detection of cervical cancer. Method: Narrative literature review conducted in the MEDLINE database using the Virtual Health Library, employing standardized descriptors from DeCS. After searching, filtering, and reviewing study eligibility, 9 articles comprised the final sample. Results: The machine learning techniques identified in the studies, including convolutional neural networks, supervised algorithms, and hybrid methods, demonstrated high accuracy rates in identifying cervical lesions. The findings also indicated a reduction in diagnostic subjectivity, greater standardization, and potential expansion of access to screening in regions with low infrastructure, as well as improved speed and efficiency in the diagnostic process. Final considerations: Machine learning shows promise as a tool for the early diagnosis of cervical cancer, potentially strengthening public policies and reducing inequalities in healthcare. However, its implementation requires local validations, mitigation of algorithmic biases, and compliance with ethical and regulatory guidelines to ensure safety and effectiveness.
Keywords: Machine Learning; Cervical Cancer; Early Diagnosis; Gynecological Nursing.
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