Automated mobile acceptance system solutions for use in special medicine

The paper presents a solution to the problem of developing an automated mobile decision-making system to assist the practicing surgeon in choosing the type of surgical repair and mathematical prediction of the performance of patients with postoperative multi-sized median hernias of the anteri-or peritoneum. A literature review was carried out on the methods and techniques of surgical treatment of hernias of the anterior peritoneal wall, on mathematical modeling of the postoperative condition of patients, on the use of artifi-cial neural networks in medical practice. Based on blood oxygen saturation, intra-abdominal pressure was assessed. The adequacy and significance of the model parameters were determined, due to the nonlinearity of the latter, by the magnitude of the relative error. For medium-sized hernias, a comparative neural network modeling of patient indicators was performed, which did not reveal the advantage of using multilayer perceptrons. The software module for neural network modeling is implemented in Python version 3.11.7 in the Spyder programming environment. We implemented a ranking of types of hernias, which can be done according to their average sizes, and the ranking of surgical methods was carried out according to their degree of complexity from 1 to 6. Thus, in the Xcode 12.5.1 environment in the Objec-tive-C language, an automated mobile system for accepting re solutions for the Apple iPhone smartphone