A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification.
The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN). To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs) and intuitionistic fuzzy cross-entropy (IFCE) with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories.
School of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, China; Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan.
Jing
J
Zhao
School of Medicine, Taipei Medical University, Taipei 100, Taiwan.
Lo-Yi
LY
Lin
School of Electrical Engineering & Automation, Xiamen University of Technology, Xiamen 361024, China; Department of Electrical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan; School of Information Science and Engineering, Xiamen University, Xiamen 361005, China.
Chih-Min
0000-0003-2107-5012
CM
Lin
D000465:Algorithms; D002531:Cerebellum / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer