Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature
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Author(s)
Gutierrez-Espinoza, Sandy
Cabanillas-Carbonell, Michael
Date
2021-12-30Subject
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Institute of Electrical and Electronics Engineers
Journal
2021 International Conference on e-Health and Bioengineering (EHB)
Additional Links
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124563830&doi=10.1109%2fEHB52898.2021.9657567&partnerID=40Abstract
At present, cervical cancer is still the most complex issue due to the fact that people who suffer from it have a high risk of death. Therefore, it is very important to have an early diagnosis. The present study is a review of the scientific literature, which includes 50 articles from the following databases: ProQuest, IEEE Xplore, PubMed, ScienceDirect, Springer, IopScience and Scopus. Thus, showing that the research that has been developed with machine learning facilitates the control, follow-up and monitoring of the disease. The systematic review shows that the model that had the highest accuracy is Convolutional Neural Network and the most used tool is R Studio, these two factors are determinant in cervical cancer, according to the research conducted with 50 articles, where more research on this topic was recorded is the continent of Asia and specifically in the countries of India and China.
Type
info:eu-repo/semantics/article
Rights
info:eu-repo/semantics/restrictedAccess
Language
eng
ISSN
2575-5145
Collections
- Ingeniería de Sistemas [300]