Propuesta de un modelo de predicción de cáncer de mama utilizando deep learning
Date
2023-11-03
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Pontificia Universidad Católica del Perú
Abstract
En la presente tesis, queremos demostrar y proponer como la tecnología puede ser
utilizada por los genetistas y especialistas en oncología como una herramienta para agilizar la
detección de cáncer de mama, siendo este el más común en Perú. El diagnóstico temprano es un
mecanismo efectivo que ayuda a la reducción de la mortalidad en este tipo de cáncer de tal
manera que se pueda seguir un tratamiento adecuado.
Actualmente una forma de detectarlo es a través de una prueba genética para identificar
mutaciones en los genes BRCA 1 y BRCA 2, sin embargo, este camino contiene pruebas que son
difíciles, costosas y lentas, que a su vez requieren una carga de trabajo excesiva por parte de un
biólogo o genetista. por tal motivo se tiene como objetivo combinar los factores de riesgo
asociados con el cáncer de mamá, incluidas las variaciones genéticas para diseñar un modelo
predictivo basados en la inteligencia artificial para determinar si el tumor asociado al cáncer es
benigno o maligno. El modelo se diseñó utilizando un algoritmo de redes neuronales logrando
obtener un rendimiento de 92% precisión con datos de prueba en tan solo unos minutos.
Esta propuesta de modelo de predicción es única en el Perú y puede ser ofrecida por una
Gerencia de TI dentro de una organización del sector salud para que posteriormente pueda ser
implementada y desplegada por un equipo de científicos de datos.
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.
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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Keywords
Mamas--Cáncer--Detección, Redes neuronales--Aplicaciones, Aprendizaje profundo
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