Application of Random Survival Forest in breast cancer survival analysis

Authors

  • 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

Keywords:

Breast Neoplasms, Machine Learning, Survival Analysis

Abstract

This paper aims to apply a supervised machine learning method to a clinical dataset from Zona da Mata Mineira, to evaluate the performance of survival prediction accuracy for breast cancer. The database utilized went through pre-processing providing the variables used in the Random Survival Forest. The results show satisfactor performance metrics for survival prediction methods. Concluding that, the machine learning methods are promising assisting and guiding clinical practice.

Author Biographies

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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Published

2023-07-20

How to Cite

Carvalho, D. S. de, Nogueira, T. da S., & Goliatt, P. V. Z. C. (2023). Application of Random Survival Forest in breast cancer survival analysis. Journal of Health Informatics, 15(Especial). https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1113

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