350 rub
Journal Dynamics of Complex Systems - XXI century №3 for 2014 г.
Article in number:
Application of bayesian approach for diagnostics of neuromuscular diseases
Authors:
N. T. Abdullaev - Ph.D. (Eng.), Associate Professor, Head of Department of Radio Systems and Television, Azerbaijani Technical University (Baku)
O. A. Dyshin - Ph.D. (Phys.-Math.), Senior Research Scientist, Azerbaijani State Oil Academy, Geotechnological Problems of Oil, Gas and Chemistry (Baku)
G. E. Abdullaeva - Dr.Sc. (Eng.), Professor, MGUPI
Abstract:
Among the statistical methods used in medicine for diagnostics of different types of a disease widely Bayesian approach is used. At application of a Bayesian method for diagnostics of a number of neuromuscular diseases the main problem in case of lack of a statistical database consists in calculation of conditional probabilities of values of diagnostic signs of the received simptomokompleks of the specific patient. It is offered to define conditional probabilities of values of diagnostic signs from the received simptomokompleks on the basis of rules of the indistinct produktion, fuzzy logic used in technology. The received values are used in Bayes's formula for calculation of probability of an illness at the studied patient.
Pages: 54-61
References

  1. Fukunaga A.K. Vvedenie v statisticheskuyu teoriyu raspoznavaniya obrazov / Per. s angl. M.: Nauka. 1979. 368 s.
  2. Patrik E'. Osnovy' teorii raspoznavaniya obrazov / Per. s angl. M.: Sov. radio. 1980. 408 s.
  3. Lewis R.J., Wears R.L. An introduction to the Bayesian analysis of clinical trials // Ann. Emerg Med. 1993. V. 22. P.1328-1336.
  4. Abrams K., Ashby D., Errington D. Simple Bayesian analysis in clinical trials: a tutorial // Control Clin. Trials. 1994. V. 15. P. 349-359.
  5. Spiegelhalter D.J., Myles J.P., Jones D.R., Abrams K.R. Bayesian methods in health technology assessment: a review // Health Technol. Assess. 2000. V. 4. P. 1-30.
  6. Zhmudyak M.L., Povalixin A.N., Strebukov A.V. i dr. Diagnostika zabolevanij metodami teorii veroyatnostej. Barnaul: Izd-vo AltGTU. 2006. 168 s.
  7. Zadeh L.A. Fuzzy sets // Inf. and Contr. 1965. V.8. P.338-353.
  8. Zadeh L.A. The linguistic approach and its application to decision analysis. In Directions in Large-Scale Systems, Y.C.Ho and S.K.Miller. Eds. New York: Plenum. 1976. P.339-370.
  9. Gext B.M., Il'ina N.A. Nervno-my'shechny'e bolezni. M.: Mediczina. 1982. 352s.
  10. Gext B.M. Teoreticheskaya i klinicheskaya e'lektromiografiya. L.: Nauka. Leningrad. otd-e. 1990. 203 s.
  11. Kasatkina L.F., Gil'vanova O.V. E'lektromiograficheskie metody' issledovaniya v diagnostike nervno-my'shechny'x zabolevanij. Igol'chataya e'lektromiografiya. M.: Medika. 2010. 416 s.
  12. Abdullaev N.T., Ismajlova K.Sh. Oczenka informaczionnoj dostovernosti diagnosticheskix zaklyuchenij v e'lektromiografii s pomoshh'yu metoda nechetkogo logicheskogo vy'voda // Informaczionno-izmeritel'ny'e i upravlyayushhie sistemy'. 2013. T. 11. № 3. S. 68-74.
  13. Loktyuxin V.N., Mal'chenko S.I., Cherepnin A.A. Osnovy' matematicheskogo obespecheniya podderzhki diagnosticheskix reshenij v biotexnicheskix sistemax s ispol'zovaniem nechetkoj logiki. Ryazan': Izd-vo Ryazan. gos. radiotexn. un-ta. 2009. 64 s.
  14. Cherepnin A.A. Modeli, algoritmy' i sredstva podderzhki prinyatiya diagnosticheskix reshenij pri e'ndoskopicheskom obsledovanii na osnove texnologii nechetkoj logiki. Diss. ... kand. texn. nauk. Ryazan'. 2010. 169 s.
  15. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB u fuzzy TECH. SP.b.: BXV-Peterburg. 2005. 736 s.