A preliminary study on automatic breast cancer data classification using semi-supervised fuzzy c-means
Daphne Teck Ching Lai, Jonathan M. Garibaldi · International Journal of Biomedical Engineering and Technology · 2013
Soria et al. have successfully identified six clinically useful and novel subgroups in the Nottingham Tenovus Breast Cancer (NTBC) data set. However, the methodology used is semi-manual and no single clustering can automatically classify the data set so far. In this work, two variations of semisupervised Fuzzy c-Means (ssFCM) algorithms are explored to classify the NTBC data set into the same six subgroups. Three experiments were conducted using the two ssFCM algorithms and the results are evaluated by using interrater agreement measures. The ssFCM algorithms identified the six classes of breast cancer but it is in low agreement with Soria’s classification. This, together with high agreement using two clustering algorithms, suggests that the problem may lie in the way we use ssFCM rather than in model correctness. Despite this, we consider the ssFCM results promising and note that work for further investigation in ssFCM is required.