Robust breast cancer classification based on GA optimized ANN and ANFIS-voting structures

With rising cancer rates in world, it is important to incorporate all possible ways in order to prevent, detect, and cure this disease. Breast cancer presents one of those threats, and bioinformatics field must work towards finding models to fight against it, with one of them being creation of classification model for that kind of illness. Using machine learning techniques in order to make these classifications is one of those ways. It is widely known that ANN (Artificial Neural Network) and ANFIS (Adaptive Neurofuzzy Inherent System) can significantly upgrade any kind of classification process, and in that way, help in biomedicine and cancer treatment. Furthermore, more objective models for classification must be developed, regarding both time and resources, in order to get optimal results. In this paper, GA (Genetic Algorithm) algorithm that optimize ANN and ANFIS has been used to make classification of breast cancer diagnosis. It is shown that GA optimization of ANFIS and ANN parameters results in creating model with better accuracy comparing to basic classifiers. Voting method has been used on such GA ANFIS optimized structure, in order to achieve model with higher reliability. Final score of computed models was determined using external validation, based on 4 most relevant clinical metrics: sensitivity, specificity, accuracy and precision.