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Journal of Integrative Medicine ›› 2022, Vol. 20 ›› Issue (3): 252-264.doi: 10.1016/j.joim.2022.02.007

• Original Experimental Research • Previous Articles     Next Articles

Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points

Sergey Filist a, Riad Taha Al-Kasasbeh b, Olga Shatalova a, Nikolay Korenevskiy a, Ashraf Shaqadan c, Zeinab Protasova a, Maksim Ilyash d, Mikhail Lukashov e   

  1. a Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation
    b Electrical Energy Department, Balqa Applied University, Amman 11937, Jordan
    c Civil Engineering Department, Zarqa University, Zarqa Governorate 13222, Jordan
    d Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint-Petersburg 197101, Russian Federation
    e Pediatric Faculty, Kursk State Medical University, Kursk 305041, Russian Federation
  • Received:2020-09-11 Accepted:2022-01-26 Online:2022-05-09 Published:2022-02-26
  • Contact: Riad Taha Al-Kasasbeh

This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery.

A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia.

Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%.

The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.

Key words: Biologically active point, Acupuncture, Current-voltage characteristic, Descriptor, Neural network

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