Comparison of neuro-fuzzy based techniques in nasopharyngeal carcinoma recurrence prediction

• Main Authors: Orrawan Kumdee, Hirosato Seki, Hiroaki Ishii, Thongchai Bhongmakapat, Panrasee Ritthipravat
• Other Authors: Mahidol University
• Format: Conference or Workshop Item
• Published: 2018
• Subjects: Computer Science; Mathematics
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/27450

This paper aims to compare neuro-fuzzy based techniques for effective prediction of nasopharyngeal carcinoma (NPC) recurrence. The techniques include an artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), the functional-type single input rule modules connected fuzzy inference method (F-SIRMs method) and the functional and neural network type SIRMs method (F-NN-SIRMs method). All models are produced to predict the presence or absence and timing of the NPC recurrence. Five years predictions are carried out. Validity of each predictive model is assured by 10-fold cross validation. The results show that the F-NN-SIRMs method is superior to the other techniques in a sense that it provides the higher prediction performance. ©2009 IEEE.