Personalizing breast cancer patients with heterogeneous data

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Pedro Henriques Abreu (Author) Other; Hugo Amaro (Author) Other; Daniel Castro Silva (Author) FEUP; Penousal Machado (Author) Other; Miguel Henriques Abreu (Author) Other

Conference proceedings International

Title: IFMBE Proceedings

Pages: 39-42

International Conference on Health Informatics, ICHI 2013

7 November 2013 through 9 November 2013

Indexing

Scopus - 6 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-00A-BBH

DOI: 10.1007/978-3-319-03005-0_11

Abstract (EN): 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. © 2014, Springer International Publishing Switzerland.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4