ANN as justified granular computing mechanism for medical data classification

The medical data and its classification have to be treated in particular way. The data should not be modified or altered, because this could lead to false decisions. Most state-of-the-art classifiers are using random factors to produce higher overall accuracy of diagnosis, however the stability of classification can vary significantly. Medical support systems should be trustworthy and reliable, therefore this paper proposes fusion of multiple classifiers based on artificial Neural Network (ANN). The structure selection of ANN is performed using granular paradigm, where granulation level is defined by ANN complexity. The classification results are merged using voting procedure. Accuracy of the proposed solution was compared with state-of-the-art classifiers using real medical data coming from two medical datasets. Finally, some remarks and further research directions have been discussed.