Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature

Sandy Gutierrez-Espinoza; Michael Cabanillas-Carbonell

doi:10.1109/EHB52898.2021.9657567

Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature

Sandy Gutierrez-Espinoza
, Michael Cabanillas-Carbonell

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2021 9th E-Health and Bioengineering Conference, EHB 2021
ISBN (Electronic)	9781665440004
DOIs	https://doi.org/10.1109/EHB52898.2021.9657567
State	Published - 2021
Externally published	Yes
Event	9th IEEE International Conference on E-Health and Bioengineering Conference, EHB 2021 - Iasi, Romania Duration: 18 Nov 2021 → 19 Nov 2021

Publication series

Name	2021 9th E-Health and Bioengineering Conference, EHB 2021

Conference

Conference	9th IEEE International Conference on E-Health and Bioengineering Conference, EHB 2021
Country/Territory	Romania
City	Iasi
Period	18/11/21 → 19/11/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

cervical cancer
diagnosis
machine learning
systematic review

Access to Document

10.1109/EHB52898.2021.9657567

Cite this

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title = "Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature",

abstract = "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.",

keywords = "cervical cancer, diagnosis, machine learning, systematic review",

author = "Sandy Gutierrez-Espinoza and Michael Cabanillas-Carbonell",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 9th IEEE International Conference on E-Health and Bioengineering Conference, EHB 2021 ; Conference date: 18-11-2021 Through 19-11-2021",

year = "2021",

doi = "10.1109/EHB52898.2021.9657567",

language = "English",

series = "2021 9th E-Health and Bioengineering Conference, EHB 2021",

booktitle = "2021 9th E-Health and Bioengineering Conference, EHB 2021",

}

Gutierrez-Espinoza, S & Cabanillas-Carbonell, M 2021, Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature. in 2021 9th E-Health and Bioengineering Conference, EHB 2021. 2021 9th E-Health and Bioengineering Conference, EHB 2021, 9th IEEE International Conference on E-Health and Bioengineering Conference, EHB 2021, Iasi, Romania, 18/11/21. https://doi.org/10.1109/EHB52898.2021.9657567

Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature. / Gutierrez-Espinoza, Sandy; Cabanillas-Carbonell, Michael.
2021 9th E-Health and Bioengineering Conference, EHB 2021. 2021. (2021 9th E-Health and Bioengineering Conference, EHB 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature

AU - Gutierrez-Espinoza, Sandy

AU - Cabanillas-Carbonell, Michael

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 - https://www.scopus.com/pages/publications/85124563830

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 -

Machine Learning Analysis for Cervical Cancer Prediction, a Systematic Review of the Literature

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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