Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

• Published in: International journal of intelligent computing and cybernetics 2017-06, Vol.10 (2), p.166-182
• Main Authors: Khan, Shabia Shabir, Quadri, S.M.K
• Format: Article
• Language: English
• Subjects: Adaptive systems; Algorithms; Artificial intelligence; Artificial neural networks; Automation; Cancer; Classification; Clustering; Colleges & universities; Computer science; Computer simulation; Computers; Construction; Datasets; Decision making; Error analysis; Expert systems; Fuzzy logic; Fuzzy sets; Fuzzy systems; Inference; Intelligence; International conferences; Knowledge; Mathematical models; Mean square values; Methods; Neural networks; Pattern recognition; Predictions; Robotics; Surgery; Survival
• ISSN: 1756-378X; 1756-3798
• DOI: 10.1108/IJICC-06-2016-0021

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with dif