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Use of Machine Learning in the diagnosis and treatment of cervical cancer: A quantitative assessment of your main conditioning factors

Abstract:

Cervical cancer is the second highest incidence and third most fatal in women. However, its prevention in early stages enables its cure. The objective of the research is to validate the main conditioning factors of cervical cancer, through a quantitative evaluation with statistical methods, which allows the effective use of machine learning techniques for the diagnosis and treatment of this disease. The research is mixed, with a non-experimental design and explanatory scope. An intentional non-representative sample of the population (n = 31) is used. The results obtained demonstrate the design of a valid instrument (CVI = 0.79), reliable factors for cervical cancer. The research constitutes the basis for the development of a computational model that contributes to the prevention of this disease, as well as its diagnosis and timely treatment.