Authors:
Hazlina Hamdan
and
Jonathan M. Garibaldi
Affiliation:
The University of Nottingham, United Kingdom
Keyword(s):
Adaptive neuro-fuzzy inference system, Survival analysis, Breast cancer, Nottingham prognostic index.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neuro-Fuzzy Systems
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Fuzzy inference systems have been applied in recent years in various medical fields due to their ability to obtain good results featuring white-box models. Adaptive Neuro-Fuzzy Inference System (ANFIS), which combines adaptive neural network capabilities with the fuzzy logic qualitative approach, has been previously used in modelling survival of breast cancer patients based on patient groups derived from the Nottingham Prognostic Index (NPI), as discussed in our previous paper. In this paper, we extend our previous work to examine whether the ANFIS model can be trained to better match the data with the NPI variable represented as a real number, rather than a categorical group. Two input models have been developed and trained with different structures of ANFIS. The performance of these models, in the capability to predict the survival rate in survival of patients following operative surgery for breast cancer, is examined.