Abstract
Anesthesia, an utmost important activity in operation theater, solely depends upon anesthesiologist, an expert. In the case of absence of expertise, drug dosing may go under-dose or overdose. To overcome this problem, an expert-based system can be designed to guide newcomers in the field of anesthesia. This structure is called as decision support system. As this system is dependent on experts’ knowledge base, its performance depends on the expert’s expertise which can be validated by comparison with other expert’s knowledge base and finding maximum correlation among them. This paper demonstrates the application of prehistoric Gower’s coefficient to validate the expert’s expertise for fuzzy logic-based experts’ system. Database is collected from ten experts. For the 80% level of confidence, eight experts are classified into one group leaving two aside. Database of these eight experts is used for the design of decision support system. A set of 270 results noted from decision support system is validated from the expert. Out of 270, expert declines 3 decisions accepting 98.88% result.
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References
Zadeh, Lotfi A. 1965. Fuzzy Sets. Information and Control 8 (3): 338–353.
Bhole, K.A., S.D. Agashe, D.N. Sonawane, Vinayak Desurkar, and Ashok Deshpande. 2011. FPGA Implementation of type I Fuzzy Inference System for Intravenous Anesthetic Drug Delivery. In World Conference on Soft Computing, May 23–26.
Kalyani Bhole, Sudhir Agashe, and Ashok Deshpande. 2013. FPGA Implementation of Type 1 Fuzzy Inference System for Intravenous Anesthesia. In IEEE International Conference on Fuzzy Systems (FUZZ). IEEE.
Bhole, Kalyani, and Sudhir Agashe. 2015. Automating Intravenous Anesthesia with a Fuzzy Inference System Coupled with a Proportional Integral Derivative (PID) Controller. American Journal of Biomedical Science and Engineering 1 (6): 93–99.
Elmore, Kimberly L., and Michael B. Richman. 2001. Euclidean Distance as a Similarity Metric for Principal Component Analysis. Monthly Weather Review 129 (3): 540–549.
Johanyák, Zsolt Csaba, and Szilvester Kovács. 2005. Distance Based Similarity Measures of Fuzzy Sets. In Proceedings of SAMI 2005.
Pal, Asim, et al. 2014. Similarity in Fuzzy Systems. Journal of Uncertainty Analysis and Applications 2 (1): 18.
Kóczy, LászlóT, and Kaoru Hirota. 1993. Ordering, Distance and Closeness of Fuzzy Sets. Fuzzy Sets and Systems 59 (3): 281–293.
Gower, John C. 1971. A General Coefficient of Similarity and Some of Its Properties. Biometrics 857–871.
Ross, Timothy J. 2009. Fuzzy Logic with Engineering Applications. Wiley.
Alvis, J.M., J.G. Reves, J.A. Spain, and L.C. Sheppard. Computer Assisted Continuous Infusion of the Intravenous Analgesic Fentanyl During General Anaesthesia—An Interactive System. IEEE Transtions on Biomedical Engineering, BME-32 (5): 323–329.
Hemmerling, Thomas M. 2011. Decision Support Systems in Anesthesia, Emergency Medicine and Intensive Care Medicine. INTECH Open Access Publisher.
Raha, Swapan, Nikhil Ranjan Pal, and Kumar Sankar Ray. 2002. Similarity-Based Approximate Reasoning: Methodology and Application. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 32 (4): 541–547.
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Bhole, K., Agashe, S., Wadgaonkar, J. (2018). How Expert is EXPERT for Fuzzy Logic-Based System!. In: Reddy, M., Viswanath, K., K.M., S. (eds) International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications . Advances in Intelligent Systems and Computing, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-5272-9_3
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DOI: https://doi.org/10.1007/978-981-10-5272-9_3
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