Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks.

Journal: Computer methods in biomechanics and biomedical engineering
PMID:

Abstract

The work investigates neural network model for prediction of post-surgical treatment risks. The descriptors of the risk classifiers are formed on the basis of the analysis of the current-voltage characteristics of one, two and three biologically active points. The training and verification samples were formed by examining 120 patients with a diagnosis of benign prostatic hyperplasia. Of these, 62 patients were successfully operated on (class C1), 30 had various complications after surgery (class C2), 28 patients required additional treatment (class C3). The constructed classifiers showed a high quality of predicting critical conditions during surgical treatment.

Authors

  • Olga Shatalova
    Department of Biomedical Engineering, Southwest State University, Kursk, Russian Federation.
  • Sergey Filist
    Department of Biomedical Engineering, Southwest State University, Kursk, Russian Federation.
  • Nikolay Korenevskiy
    Department of Biomedical Engineering, Southwest State University, Kursk, Russian Federation.
  • Riad Taha Al-Kasasbeh
    Electrical Engineering Department, Balqa Applied University.
  • Ashraf Shaqadan
    Civil Engineering Department, Zarqa University.
  • Zeinab Protasova
    Department of Biomedical Engineering, Southwest State University, Kursk, Russian Federation.
  • Maksim Ilyash
    Mechanics and Optics, Saint-Petersburg National Research University of Information Technologies, Russian Federation.
  • Anatoly Rybochkin
    Department of Space Instrumentation Tel, Southwestern State University, Kursk, Russian Federation.