10.35940/ijrte.C4316.099320
https://zenodo.org/records/5842846
oai:zenodo.org:5842846
Elmer Diaz
Elmer Diaz
Software Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.
Andres Ccopa
Andres Ccopa
Software Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.
Lenis Wong
Lenis Wong
Software Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.
Cervical Cancer: Machine Learning Techniques for Detection, Risk Factors and Prevention Measures
Zenodo
2020
Cervical cancer, Cervical cancer diagnosis, Machine learning.
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
2020-09-30
eng
2277-3878
Creative Commons Attribution 4.0 International
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.