350 rub
Journal Biomedical Radioelectronics №1 for 2014 г.
Article in number:
Nonconventional use of Bayesian formulas for differential diagnostics of neuromuscular diseases
Authors:
N.T. oglu Abdullayev - Ph.D.(Eng.), Associate Professor, Azerbaijan Technical University. E-mail: a.namik46@mail.ru
O.А. Dyšin - Ph.D.( Phys.-Math.), Senior Research Scientist, Azerbaijan State Oil Academy Scientific Research Institute «Geotechnological problems of oil, gas and chemistry»
G.E. gizi Abdullayeva - Author, Azerbaijan State Academy
Abstract:
The main difficulty of application of Bayesian methods for differential diagnostics of diseases is presented by calculation of conditional probabilities of values of diagnostic signs for this simptomokompleks at the patient at each concrete disease from set of the diagnosed. For these purposes it is offered to use system of the indistinct logical conclusion, allowing to receive approximate values of the validity of each of diagnosed diseases for the set simptomokompleks of values of diagnostic signs of the studied patient and on their basis to calculate conditional probabilities of signs. Use of these calculated values in iterative Bayesian procedures gives the chance of establishment of more reliable estimates of existence at the patient of diagnosed diseases.
Pages: 50-58
References

  1. Gext B.M., Il'ina N.A. Nervno-my'shechny'e bolezni. M.: Mediczina. 1982. 352 s.
  2. Gext B.M. Teoreticheskaya i klinicheskaya e'lektromiografiya. L.: Nauka. 1990. 203 s.
  3. 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.
  4. Zhmudyak M.L., Povalixin A.N., Strebukov A.V. i dr. Diagnostika zabolevanij metodami teorii veroyatnostej. Barnaul, Izd-vo AltGTU, 2006. -168s.
  5. Zhmudyak M.L., Povalixin A.N., Lev G.Sh. Primenenie veroyatnostny'x metodov v diagnostike. Sb. materialov konf. «Diskretny'j analiz i issledovanie operaczij». Novosibirsk: Izd-vo Instituta matematiki. 2004. S. 203.
  6. Fukunaga A.K. Vvedenie v statisticheskuyu teoriyu raspoznavaniya obrazov: per. s angl. M.: Nauka. 1979. 368 s.
  7. Patrik E'. Osnovy' teorii raspoznavaniya obrazov: per. s angl. M.: Sov. radio. 1980. 408 s.
  8. 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.
  9. Cherepnin A.A. Modeli, algoritmy' i sredstva podderzhki prinyatiya diagnosticheskix reshenij pri e'ndoskopicheskom obsledovanii na osnove texnologii nechetkoj logiki: Diss. na soisk. uch. step. kand. texnich. nauk. Ryazan'. 2010. 169 s.
  10. Zadeh L.A. Fuzzy sets // Inf. and Contr. 1965. V. 8. P. 338-353.
  11. 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.
  12. Zel'ner A. Bajesovskie metody' v e'konometrii: per.s angl. M.: Statistika. 1980. 438 s.
  13. Xej Dzh. Vvedennie v metody' bajesovskogo statisticheskogo vy'voda: per. s angl. M.: Finansy' i statistika. 1987. 335 s.
  14. 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'. 2012. T. 10. № 4. S. 60-66.
  15. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLABufuzzyTECH. SP.b.: BXV-Peterburg. 2005. 736 s.
  16. Bejli N. Statisticheskie metody' v biologii: per. s angl. M.: IIL. 1962. 271 s.
  17. Niczy'n D.A. Model' predstavleniya priznakov v bajesovskom klassifikatore mediczinskix izobrazhenij // Vestnik NTU «XPI». Tematicheskij vy'pusk: Informatika i modelirovanie. Xar'kov: NTU «XPI». 2008. № 49. S. 105-113.