An Intelligent Disease Diagnosis System by Fuzzy Similarity Distance

Abstract:

The progress of Artificial intelligence and Information technology has been driving research of intelligent diagnosing, and the efficiency of diagnosis is improved by disease diagnosing system .In this paper, we analyze the comparability and relativity between fuzzy similarity distance and disease diagnosis, and initiate the theoretical model of fuzzy-distance on the basis of fuzzy membership factors vectors, and some corresponding data structure. In addition, the inference algorithm and the hierarchy of system are brought forward. Experimentation statistics demonstrate that the novel diagnosis system could obtain a satisfying accuracy rate of diagnosis, and low a rate of misdiagnosis effectively.

• Periodical: Applied Mechanics and Materials (Volumes 71-78)
• Pages: 2218-2221
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.71-78.2218
• Online since: July 2011
• Authors: Wen Xue Tan, Mei Sen Pan, Xiao Rong Xu
• Keywords: Disease, Fuzzy Similarity Distance, Intelligent Diagnosing, Symptom

References

[1] Lu Feng, Huang Jinquan. Fault Diagnosis for Aero-engine Based on ESVR Information Fusion , Journal Of Basic Science And Engineering，Vol.18(2010), p.982.

[2] Zhang Peng, Huang Jinquan. Aeroengine fault diagnosis using dual Kalman filtering technique. Journal of Aerospace Power, Vol.23 (2008).P.952.

[3] Tan Wenxue, Chen Hongbo, Guo Guoqiang, et al. Multi-engine Animal Disease Diagnostic Expert System, Computer Engineering，Vol.34 (2008), P.193.

[4] Chen Tian, Sun Jianguo, Hao Ying. Neural network and dempster shafter theory based fault diagnosis for aeroengine gas path. Acta Aeronautica et Astronautica Sinica, Vol.27(2006):1014..

[5] Li Ran，Qiu Xiaolong．Improvement in fault diagnosis of transmission networks using fuzzy Petri net．China Electric Power, Vol.41(2008),P.50.

[6] Xi Jin-ju, Li Shuhong, Research on sheep disease diagnostic expert system based on subjective Bayes algorithm with self-learning , Journal of Hunan University of Science ＆ Technology：Natural Science Edition，Vol.24(2009),p.95.

[7] Srinivas Mukkamala, Andrew H. Sung. Identifying significant feature for network forensic analysis using artificial intelligence techniques. International Journal of Digital Evidence, Vol.1 (2003), p.543.

[8] Xiao Shanghais, Xiang Xuejun, Lei Bangjun, ET at, Research on a dynamic recurrent Elman neural network model for electric load forecasting and its Wilcoxon test, Journal of Computational Information Systems, Vol.6 (2010), p.959.

[9] Ding Shi-fei,Jin Feng-xiang,Shi Zhong-zhi, Information feature compression based on partial least squares, Journal of Computer Aided Design & Computer Graphics,Vol.17(2005), p.368.

DOI: 10.1109/icmlc.2005.1527229