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Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 720–736 | Cite as

On Some Transformations of Features in Machine Learning in Medicine

  • Yu. I. Zhuravlev
  • V. V. Ryazanov
  • O. V. Sen’ko
  • A. A. Dokukin
  • P. A. Afanas’ev
Mathematical Method in Pattern Recognition

Abstract

A new view is given to supervised classification problems by precedents on the basis of logical approaches and the possibility of their application in medicine. The basic logical and logical statistical models of classification (basic definitions, search, processing, and application of logical regularities of classes (LRCs); transition to other feature spaces; and the method of optimal reliable decompositions) and their verification are presented. Numerous applications in medicine and two problems of qualification assessment and choice of the treatment method are considered.

Keywords

classification recognition feature decomposition algorithm logical regularity of a class 

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References

  1. 1.
    A. N. Dmitriyev, Y. I. Zhuravlev, and F. P. Krendelev, “On mathematical principles for classification of objects and phenomena,” in Diskret. Analiz (Inst. Math. Siberian Branch Acad. Sci. USSR, Novosibirsk, 1966), No. 7, pp. 3–15 [in Russian].Google Scholar
  2. 2.
    Yu. I. Zhuravlev and V. V. Nikiforov, “Recognition algorithms based on computation of estimates,” Cybern. 7 (3), 387–400 (1971).CrossRefGoogle Scholar
  3. 3.
    M. N. Vaintsvaig, “Kora: A learning pattern recognition algorithm,” in Algoritmy obucheniya raspoznavaniyu obrazov (Learning Algorithms in Pattern Recognition), Ed. by V. N. Vapnik (Sov. Radio, Moscow, 1973), pp. 110–116 [in Russian].Google Scholar
  4. 4.
    Yu. I. Zhuravlev, V. V. Ryazanov, and O. V. Sen’ko, Recognition. Mathematical Methods. Program System. Practical Applications (Fazis, Moscow, 2006) [in Russian].Google Scholar
  5. 5.
    V. V. Ryazanov, “Logical regularities in pattern recognition (parametric approach),” Comput. Math. and Math. Phys. 47 (10), 1720–1735 (2007).MathSciNetCrossRefGoogle Scholar
  6. 6.
    N. V. Kovshov, V. L. Moiseev, and V. V. Ryazanov, “Algorithms for finding logical regularities in pattern recognition,” Comput. Math. and Math. Phys. 48 (2), 314–328 (2008).MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Yu. I. Zhuravlev, L. A. Aslanyan, and V. V. Ryazanov, “Analysis of a training sample and classification in one recognition model,” Pattern Recogn. Image Anal. 24 (2), 347–352 (2014).CrossRefGoogle Scholar
  8. 8.
    R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd. ed. (Wiley, New Delhi, 2000).Google Scholar
  9. 9.
    W. W. Cohen and Y. Singer, “A simple, fast, and effective rule learner,” in Proc. AAAI–99/IAAI–99 16th National Conf. on Artificial Intelligence and 11th Conf. on Innovative Applications of Artificial Intelligence (Orlando, FL, 1999), pp. 335–342.Google Scholar
  10. 10.
    D. P. Vetrov and V. V. Ryazanov, On the minimization of the feature space in recognition problems,” in Mathematical Methods for Pattern Recognition (MMPR–10), Proc. 10th All–Russian Conf. (Vych. Tsentr, Ross. Akad. Nauk, Moscow, 2001), pp. 22–25 [in Russian].Google Scholar
  11. 11.
    Yu. I. Zhuravlev, “Correct algebras over sets of incorrect (heuristic) algorithms,” I, Cybern. 13 (4), 489–497 (1977); II, Cybern. 13 (6), 814–821 (1977).MathSciNetzbMATHGoogle Scholar
  12. 12.
    Yu. I. Zhuravlev, “On an algebraic approach to the solution of pattern recognition and classification problems,” Problemy Kibernet. No. 33, 5–68 (1978) [in Russian].Google Scholar
  13. 13.
    S. S. Lvov and V. V. Ryazanov, “On a method of multialgorithmic classification,” Int. J. “Information Theories and Applications” 22 (2), 117–141 (2015) [in Russian].Google Scholar
  14. 14.
    O. L. Mangasarian and W. H. Wolberg, “Cancer diagnosis via linear programming,” SIAM News 23 (5), 1–18 (1990).Google Scholar
  15. 15.
    T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error–correcting output codes,” J. Artif. Intell. Res. 2, 263–286 (1995).CrossRefzbMATHGoogle Scholar
  16. 16.
    A. A. Dokukin, V. V. Ryazanov, and O. V. Shut, “Multilevel models for solution of multiclass recognition problem,” Pattern Recogn. Image Anal. 26 (3), 461–473 (2016).CrossRefGoogle Scholar
  17. 17.
    V. V. Ryazanov, “Optimisation of multiclass supervised classification based on using output codes with errorcorrecting,” Pattern Recogn. Image Anal. 26 (2), 262–265 (2016).CrossRefGoogle Scholar
  18. 18.
    A. V. Kuznetsova, I. V. Kostomarova, and O. V. Sen’ko, “Modification of the method of optimal valid partitioning for comparison of patterns related to the occurrence of ischemic stroke in two groups of patients,” Pattern Recogn. Image Anal. 24 (1), 114–123 (2014).CrossRefGoogle Scholar
  19. 19.
    H. Ganster, M. Gelautz, A. Pinz, M. Binder, H. Pehamberger, M. Bammer, and J. Krocza, “Initial results of automated melanoma recognition,” in Theory and Applications of Image Analysis II, Selected Papers from the 9th Scandinavian Conference on Image Analysis (Uppsala, Sweden, June 1995), Ed. by G. Borgefors (World Scientific, Singapore, 1995) pp. 343–354.CrossRefGoogle Scholar
  20. 20.
    UCI Machine Learning Repository. School of Information and Computer Science, University of California, Irvine, CA, 2007. http://www.ics.uci.edu/~mlearn/MLRepository.htmlGoogle Scholar
  21. 21.
    Yu. I. Zhuravlev, I. B. Petrov, and V. V. Ryazanov, “Discrete methods for diagnostics and analysis of medical data,” in Meditsina v zerkale informatiki (Medicine in the Mirror of Informatics), Ed. by O. M. Belozerkovsky and A. S. Cholodov (Nauka, Moscow, 2008), pp. 113–123 [in Russian].Google Scholar
  22. 22.
    Yu. Alyaev, G. Kuz’micheva. M. Kolesnikova, V. Ryazanov, V. Rudenko, D. Mel’nikov, and A. Levko, “Investigation of the composition of urinary stones in vivo using modern information technologies,” Vrach (Doctor), No. 1, 19–22 (2009) [in Russian].Google Scholar
  23. 23.
    Yu. I. Zhuravlev, G. I. Nazarenko, V. V. Ryazanov, and A. M. Cherkashov, “Predicting outcomes of operative therapy of degenerative diseases of the lumbar spine,” Vestnik travmatologii i ortopedii imeni N.N. Priorova, No. 2, 3–10 (2008) [in Russian].Google Scholar
  24. 24.
    Yu. I. Zhuravlev, G. I. Nazarenko, V. V. Ryazanov, and E. B. Kleimenova, “Novel method of risk analysis for the development of ischemic heart disease with the use of genomic and computer technologies,” Kardiologiya (Cardiology) 51 (2), 19–25 (2011) [in Russian].Google Scholar
  25. 25.
    G. I. Nazarenko, Yu. I. Zhuravlev, V. V. Ryazanov, M. V. Konstantinova, and E. B. Kleimenova, “Prediction of the risk of developing ischemic stroke on the basis of genetic [ApoE (ε2, ε3, ε4), ACE (I/D), MTHFR (677CT)] and non–genetic factors with the use of modern methods of the theory of recognition by precedents,” Molekulyarnaya meditsina (Molecular Medicine), No. 1, 55–60 (2015) [in Russian].Google Scholar
  26. 26.
    Yu. I. Zhuravlev, G. I. Nazarenko, A. P. Vinogradov, A. A. Dokukin, N. N. Katerinochkina, E. B. Kleimenova, M. V. Konstantinova, V. V. Ryazanov, O. V. Sen’ko, and A. M. Cherkashov, “Methods for discrete analysis of medical data on the basis of recognition theory and some of their applications,” Pattern Recogn. Image Anal. 26 (3), 643–664 (2016).CrossRefGoogle Scholar
  27. 27.
    D. V. Podkopaev, Assessment of the Effectiveness of Treatment of Patients with Arterial Hypertension at the Outpatient Stage, Ph.D. thesis, Central Research Institute of Gastroenterology, Russian Academy of Medical Sciences, 2012 [in Russian].Google Scholar
  28. 28.
    O. V. Sen’ko, V. Ya. Chuchupal, and A. A. Dokukin, “Non–invasive arterial pressure estimating with the cardiac monitor CardioQvark,” Mat. Biolog. Bioinform. 12 (2), 536–546 (2017) [in Russian].CrossRefGoogle Scholar
  29. 29.
    T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, New York, 2009).CrossRefzbMATHGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • Yu. I. Zhuravlev
    • 1
  • V. V. Ryazanov
    • 1
  • O. V. Sen’ko
    • 1
  • A. A. Dokukin
    • 1
  • P. A. Afanas’ev
    • 2
  1. 1.Dorodnitsyn Computing Center, Federal Research Center “Informatics and Control”Russian Academy of SciencesMoscowRussia
  2. 2.Faculty of Computational Mathematics and CyberneticsMoscow State UniversityMoscowRussia

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