
## 4 Conclusion

The foregoing models are aimed at developing more complete models that are useful for the development of a reliable diagnosis decision model. 

Important information nets usually assumed away in previous mathematical models are explicitly considered and modeled via fuzzy set theory. 

## References

[1] A.O. Esogbue, V. Aggrwal and D. Kajaulgi, Computer aided anesthesia administration, Int. J. Bio- Med. Comput. 7 (1976) 271-288. 

[2] A.R. Feinstein, An analysis of diagnostic reasoning: The strategy of intermediate decisions Yale Biology and medicine, 46 (1973) 264-283. 

[3] A.L.Rector and E. Ackerman, Rules for sequential diagnosis, Comput. Bio-Med. Research 8 
(1975) 143-155. [4] Atanassov,K, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986), pp 87-97. 

[5] B.S. Duran and T.O. Lesis, An application of cluster analysis to construction of a diagnostic classification. Computers in Biology and Medicine 4 (1974) 183- 188. [6] C.V. Negoita and D.A. Ralescu, Application of Fuzzy Sets to System Analysis (Wiley. New York, 1976). [7] Dubious, D. and Prade, H., "Fuzzy Sets and System: Theory and Application", Academic Press, New York . (1990). [8] Gau, W. L. and Buehrer, D. J, Vague sets, IEEE Transactions on Systems, Man and Cybernetics, Vol.(23), (1993) pp 610-614. 

[9] G.A. Gorry and G.O. Barnett, Sequential diagnosis by computer. J. Am. Med. Assoc. 205 
(1968) 849-854. [10] H.R. Warner, A.F. Toronto, L.G. Veasey and R. Stephensio. A mathematical approach to medical diagnosis, Jou.Amer. Med. Assoc. 177 (3) 
(1961) 177-184. 

JOURNAL OF COMPUTING, VOLUME 2, ISSUE 5, MAY 2010, ISSN 2151-9617 HTTPS://SITES.GOOGLE.COM/SITE/JOURNALOFCOMPUTING/ WWW.JOURNALOFCOMPUTING.ORG 112 where each rj for j = 1, 2, 3,…..,n is a defining aspect. Thus, the fuzzy mapping in Eq. (26) becomes rj z(t) …………………….(30) where μj is a membership function mapping rt1, rt2, …… ,rtn into [0, 1]. Since these aspects rtj, j = 1,2,….n are very specific to the individual test t, generalities concerning the possible structure of μΓ (rt1, rt2,…, rtn) are difficult to make Consequently this will be investigated in a future effort. 

[11] J.A.Jarquez , Computer Diagnosis and Diagnostic Methods (Thomas, Springfield, IL 1972). 

[12] Klir, J.G. and Yuan, B., Fuzzy Sets And Fuzzy Logic: Theory and Applications, Prentice Hall of India, 1995. 

[13] M.J. Norusis and J.A. Jecquez, Diagnosis I: 
Symptom non independence in mathematical models for diagnosis. Comput, Bio-Med, research 8 (1975) 156-172. 

[14] M.J. Norusis and J.A. Jacquez, Diagnosis, II: 
Diagnostic models based on attribute clusters. A proposal and comparisons. Comput, Bio-Med. Research 8 (1975) 173-188. 

 [15] Winston, P.H., "Artificial Intelligence," Addison Wesley, India, (2000). 

[16] Zadeh. L.A., (1965); Fuzzy sets, Infor. and Control (8), pp 338-353. [17] Z.Pawlak, Rough Sets, International Journal of Info. and Comp.Sc.11 (1982) 341-356. 

First A. Author Siddhartha Sankar Biswas is M.Tech in Computer Science from Institute of Technology & management, Gurgaon and is currently working as Lecturer in Gurgaon Institute of Technology & management, Gurgaon. 