Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples

According to the results of our experimental work, a combination of functional trees and the naive bayes classifier gave the best prognostic scores indicating very good kappa agreement values (κ = 0.899 and κ = 0.949, p < 0.001) with the experts. This combination also gave the best dichotomization rate (96.3%) for assessment of estrogen receptor status. Wavelet color features provided better classification accuracy than Laws texture energy and co-occurrence matrix features.