Prediction of nasopharyngeal carcinoma recurrence by neuro-fuzzy techniques

Neuro-fuzzy techniques for prediction of nasopharyngeal carcinoma recurrence are mainly focused in this paper. A technique, named Generalized Neural Network-type Single Input Rule Modules connected fuzzy inference method is proposed. In the study, clinical data of patients with nasopharyngeal carcinoma were collected from Ramathibodi hospital, Thailand. In total, 495 records were taken into account. Relevant factors were extracted and employed in developing predictive models. The results showed that the proposed technique was superior to the other neuro-fuzzy techniques, stand-alone neural network, logistic regression and Cox proportional hazard model. Accuracy and AUC above 80% and 0.8 could be achieved. To show validity of the proposed technique, two nonlinear problems, i.e., function approximation and the XOR classification problems, are studied. Simulation results showed that the proposed technique could simplify the problem by converting the original nonlinear input into the lower complexity one. In addition, it can solve the XOR problem whereas the traditional approach cannot tackle this problem.