Aplicación del Random Survival Forest en el análisis de la supervivencia del cáncer de mama

Autores/as

  • Daniela Schimitz de Carvalho Universidade Federal de Juiz de Fora
  • Thallys da Silva Nogueira Universidade Federal de Juiz de Fora
  • Priscila Vanessa Zabala Caprile Goliatt Universidade Federal de Juiz de Fora

DOI:

https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1113

Palabras clave:

Neoplasias de mama, Machine Learning, Análisis de Supervivencia

Resumen

Este trabajo tiene como objetivo aplicar un método de aprendizaje automático supervisado a un conjunto de datos clínicos de la Zona da Mata Mineira, para evaluar el rendimiento de la precisión de la predicción de la supervivencia para el cáncer de mama. La base de datos utilizada pasó por un preprocesamiento que proporcionó las variables que se emplearían en el Random Survival Forest. Los resultados presentan métricas de rendimiento satisfactorias para los métodos de predicción de la supervivencia. Concluyendo que los métodos de aprendizaje automático son prometedores en la asistencia y orientación en la práctica clínica.

Biografía del autor/a

Daniela Schimitz de Carvalho, Universidade Federal de Juiz de Fora

Programa de Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora – UFJF, Juiz de Fora (MG), Brasil.

Thallys da Silva Nogueira, Universidade Federal de Juiz de Fora

Programa de Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora – UFJF, Juiz de Fora (MG), Brasil.

Priscila Vanessa Zabala Caprile Goliatt, Universidade Federal de Juiz de Fora

Programa de Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora – UFJF, Juiz de Fora (MG), Brasil.

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Publicado

2023-07-20

Cómo citar

Carvalho, D. S. de, Nogueira, T. da S., & Goliatt, P. V. Z. C. (2023). Aplicación del Random Survival Forest en el análisis de la supervivencia del cáncer de mama. Journal of Health Informatics, 15(Especial). https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1113

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