Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points.
10.1016/j.joim.2022.02.007
- Author:
Sergey FILIST
1
;
Riad Taha AL-KASASBEH
2
;
Olga SHATALOVA
1
;
Nikolay KORENEVSKIY
1
;
Ashraf SHAQADAN
3
;
Zeinab PROTASOVA
1
;
Maksim ILYASH
4
;
Mikhail LUKASHOV
5
Author Information
1. Department of Biomedical Engineering, Southwest State University, Kursk 305040, Russian Federation.
2. Electrical Energy Department, Balqa Applied University, Amman 11937, Jordan. Electronic address: riad_alkasasbeh@bau.edu.jo.
3. Civil Engineering Department, Zarqa University, Zarqa Governorate 13222, Jordan.
4. Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics, Saint-Petersburg 197101, Russian Federation.
5. Pediatric Faculty, Kursk State Medical University, Kursk 305041, Russian Federation.
- Publication Type:Journal Article
- Keywords:
Acupuncture;
Biologically active point;
Current-voltage characteristic;
Descriptor;
Neural network
- MeSH:
Acupuncture Points;
Acupuncture Therapy;
Fuzzy Logic
- From:
Journal of Integrative Medicine
2022;20(3):252-264
- CountryChina
- Language:English
-
Abstract:
OBJECTIVE:This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery.
METHODS: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.
RESULTS: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%.
CONCLUSION: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.
