1. Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks
- Author
-
Anatoly Rybochkin, Maksim Urievich Ilyash, S. A. Filist, Zeinab Protasova, Ashraf Adel Shaqadan, Riad Taha Al-Kasasbeh, O. V. Shatalova, and Nikolay Aleexevich Korenevskiy
- Subjects
Computer science ,0206 medical engineering ,Biomedical Engineering ,Bioengineering ,02 engineering and technology ,Post surgery ,Machine learning ,computer.software_genre ,03 medical and health sciences ,0302 clinical medicine ,Current voltage ,Humans ,Postoperative Period ,Surgical treatment ,Critical condition ,Artificial neural network ,business.industry ,030229 sport sciences ,General Medicine ,020601 biomedical engineering ,Class (biology) ,Computer Science Applications ,Human-Computer Interaction ,Prostate surgery ,Artificial intelligence ,Neural Networks, Computer ,business ,computer - 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.
- Published
- 2021