Personalizing Breast Cancer Patients with Heterogeneous Data
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.
KeywordsWomen Breast Cancer Patient Personalization Genetic Algorithm Clustering Algorithms
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