Bibliographic citations
Páez, J., Palomino, H., Rosado, C., Salazar, E. (2023). Propuesta de un modelo de predicción de cáncer de mama utilizando deep learning [Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/26346
Páez, J., Palomino, H., Rosado, C., Salazar, E. Propuesta de un modelo de predicción de cáncer de mama utilizando deep learning []. PE: Pontificia Universidad Católica del Perú; 2023. http://hdl.handle.net/20.500.12404/26346
@mastersthesis{renati/25799,
title = "Propuesta de un modelo de predicción de cáncer de mama utilizando deep learning",
author = "Salazar Huamanjulca, Elmer Ronald",
publisher = "Pontificia Universidad Católica del Perú",
year = "2023"
}
In the present thesis, we are looking for a demonstration and proposal how the technology can be so useful for the genetic and oncology Scientifics as a tool for quick detection of the breast cancer, which ones is the most common in Peru. Early diagnosis is the most effective way for a treatment to help people to prevent the mortality in this kind of cancer. At this moment, the best way for an early detection is a genetical test to look for mutations in BRCA 1 and BRCA 2 gen, however this way is so hard, because this requires a lot of difficult, expensive, and slowly tests remark a lot of work of the genetic and oncology Scientifics. That is the reason our thesis has as the principal goal to combine all the risk factors associated with breast cancer, including genetical mutations, for generate a predictive model based in artificial intelligence for determinate if a kind of tumor is associated with benign or pathogenic. This designed model has a 92% of precision with open-source test data in a few minutes. This predictive model is unique in Peru and can be offered by an IT Management within a health sector organization so that it can later be implemented and deployed by a team of data scientists.
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