Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks. Issue 13 (3rd October 2021)

• Record Type: Journal Article
• Authors: Shatalova, Olga; Filist, Sergey; Korenevskiy, Nikolay; Taha Al-kasasbeh, Riad; Shaqadan, Ashraf; Protasova, Zeinab; Ilyash, Maksim; Rybochkin, Anatoly
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 24:Issue 13(2021)
• Page Start: 1504
• Page End: 1516
• Publication Date: 2021-10-03
• Subjects: BAP -- current-voltage characteristics -- prostate surgery -- risk -- neural network; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.1895128
• Languages: English
• ISSNs: 1025-5842

Abstract

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.