Clustering-based approach for detecting breast cancer recurrence

This paper aims to assess the effectiveness of three different clustering algorithms, used to detect breast cancer recurrent events. The performance of a classical k-means algorithm is compared with a much more sophisticated Self-Organizing Map (SOM-Kohonen network) and a cluster network, closely related to both k-means and SOM. The three clustering algorithms have been applied on a concrete breast cancer dataset, and the result clearly showed that the best performance was obtained by the cluster network, followed by SOM and k-means, their predicting accuracy ranging from 62% to 78%. Based on the patients' segmentation regarding the occurrence of recurrent events, new patients may be labeled according to their medical characteristics as developing or not recurrent events, thus supporting health professionals in making informed decisions.