A hybrid intelligent system for medical diagnosis

We propose a novel hybrid intelligent system (HIS) that is a combination of numerical and linguistic knowledge representation. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a fuzzy linguistic model optimized via the genetic algorithm. The ILFN is self-organizing network with the capability of fast, online, incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy and comprehensibility. The resulted HIS is capable of dealing with low-level numerical computation and higher-level linguistic computation. After the system completely constructed, it can incrementally learn new information in both numerical and linguistic structures. To evaluate the system's performance the well-known benchmark Wisconsin breast cancer data was studied as an application to medical diagnosis. The simulation results show that the proposed HIS perform better than the individual standalone systems.

[1]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Ah-Hwee Tan,et al.  Cascade ARTMAP: integrating neural computation and symbolic knowledge processing , 1997, IEEE Trans. Neural Networks.

[3]  Wai Lam,et al.  Discovering Knowledge from Medical Databases , 2000 .

[4]  Joydeep Ghosh,et al.  Symbolic Interpretation of Artificial Neural Networks , 1999, IEEE Trans. Knowl. Data Eng..

[5]  Phayung Meesad,et al.  Pattern classification by an incremental learning fuzzy neural network , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[6]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[7]  Sorina Zahan,et al.  A fuzzy approach to computer-assisted myocardial ischemia diagnosis , 2001, Artif. Intell. Medicine.

[8]  O. Mangasarian,et al.  Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Phayung Meesad,et al.  An effective neuro-fuzzy paradigm for machinery condition health monitoring , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Constantinos S. Pattichis,et al.  Neural network models in EMG diagnosis , 1995 .

[11]  B Kovalerchuk,et al.  Consistent knowledge discovery in medical diagnosis. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[12]  Phayung Meesad,et al.  Constructing a fuzzy expert system using the ILFN network and the genetic algorithm , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[13]  Stefan Wermter,et al.  An Overview of Hybrid Neural Systems , 1998, Hybrid Neural Systems.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  José Carlos Príncipe,et al.  Incremental backpropagation learning networks , 1996, IEEE Trans. Neural Networks.

[16]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[17]  Larry R. Medsker,et al.  Hybrid Neural Network and Expert Systems , 1994, Springer US.

[18]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[19]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .