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Cervical Cancer: Machine Learning Techniques for Detection, Risk Factors and Prevention Measures
Elmer Diaz1, Andres Ccopa2, Lenis Wong3

1Elmer Y. Diaz Quiroz, Software Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.
2Andrés J. Ccopa Mamani, Software Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.
3Lenis R. Wong Portillo, Software Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.

Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 158-163 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4316099320 | DOI: 10.35940/ijrte.C4316.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cervical Cancer is considered the fourth most common female malignancy worldwide and represents a major global health challenge. As a result, in recent years, various proposals and researches have been conducted. This study aims to analyze the data presented in current researches regarding cervical cancer and contribute to future research, all through the framework of literature review, based on 3 research questions: Q1: What are the risk factors that cause cervical cancer? Q2: What preventive measures are currently established for cervical cancer? and, Q3: What are the techniques to detect cervical cancer? Findings show that detection techniques are complementary since they are categorized under machine learning. Therefore, we recommend that further study be promoted in these techniques as they are helpful in the detection process. In addition, risk factors can be considered for a greater scope in detection, such as HPV infection, since it is the most relevant factor for the development of cervical cancer. Finally, we suggest to conduct further research on preventive measures for cervical cancer. 
Keywords: Cervical cancer, Cervical cancer diagnosis, Machine learning.