TY - GEN
T1 - Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature
AU - Gutierrez-Espinoza, Sandy
AU - Cabanillas-Carbonell, Michael
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - cervical cancer
KW - diagnosis
KW - machine learning
KW - systematic review
UR - http://www.scopus.com/inward/record.url?scp=85124563830&partnerID=8YFLogxK
U2 - 10.1109/EHB52898.2021.9657567
DO - 10.1109/EHB52898.2021.9657567
M3 - Conference contribution
AN - SCOPUS:85124563830
T3 - 2021 9th E-Health and Bioengineering Conference, EHB 2021
BT - 2021 9th E-Health and Bioengineering Conference, EHB 2021
T2 - 9th IEEE International Conference on E-Health and Bioengineering Conference, EHB 2021
Y2 - 18 November 2021 through 19 November 2021
ER -
