Personalizing Breast Cancer Patients with Heterogeneous Data

  • Pedro Henriques Abreu
  • Hugo Amaro
  • Daniel Castro Silva
  • Penousal Machado
  • Miguel Henriques Abreu
Part of the IFMBE Proceedings book series (IFMBE, volume 42)

Abstract

The prediction of overall survival in patients has an important role, especially in diseases with a high mortality rate. Encompassed in this reality, patients with oncological diseases, particularly the more frequent ones like woman breast cancer, can take advantage of a very good customization, which in some cases may even lead to a disease-free life. In order to achieve this customization, in this work a comparison between three algorithms (evolutionary, hierarchical and k-medoids) is proposed. After constructing a database with more than 800 breast cancer patients from a single oncology center with 15 clinical variables (heterogeneous data) and having 25% of the data missing, which illustrates a real clinical scenario, the algorithms were used to group similar patients into clusters. Using Tukey’s HSD (Honestly Significant Difference) test, from both comparison between k-medoids and the other two approaches (evolutionary and hierarchical clustering) a statistical difference were detected (p − value < 0.0000001) as well as for the other comparison (evolutionary versus hierarchical clustering) - p − value = 0.0061354 - for a significance level of 95%.

The future work will consist primarily in dealing with the missing data, in order to achieve better results in future prediction.

Keywords

Women Breast Cancer Patient Personalization Genetic Algorithm Clustering Algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pedro Henriques Abreu
    • 1
  • Hugo Amaro
    • 1
  • Daniel Castro Silva
    • 1
  • Penousal Machado
    • 1
  • Miguel Henriques Abreu
    • 2
  1. 1.Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.Portuguese Institute of Oncology of PortoPortoPortugal

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