AN INVESTIGATION OF THE EFFECT OF INPUT REPRESENTATION IN ANFIS MODELLING OF BREAST CANCER SURVIVAL
Hazlina Hamdan, Jonathan M. Garibaldi · 2010
Intelligent Modelling and Analysis (IMA) Research Group, School of Computer ScienceThe University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, U.K.fhzh, [email protected]: Adaptive neuro-fuzzy inference system, Survival analysis, Breast cancer, Nottingham prognostic index.Abstract: Fuzzy inference systems have been applied in recent years in various medical fields due to their ability toobtain good results featuring white-box models. Adaptive Neuro-Fuzzy Inference System (ANFIS), whichcombines adaptive neural network capabilities with the fuzzy logic qualitative approach, has been previouslyused in modelling survival of breast cancer patients based on patient groups derived from the NottinghamPrognostic Index (NPI), as discussed in our previous paper. In this paper, we extend our previous work toexamine whether the ANFIS model can be trained to better match the data with the NPI variable representedas a real number, rather than a categorical group. Two input models have been developed and trained withdifferent structures of ANFIS. The performance of these models, in the capability to predict the survival ratein survival of patients following operative surgery for breast cancer, is examined.