aspg-21-1738-1791472411 20261008151331 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2023 20 4 Mohammad Kanan Industrial Engineering Department, College of Engineering, University of Business and Technology, Jeddah 23847, Saudi Arabia Nadir Omer Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha 61922, P. O. Box 551, Saudi Arabia Safaa S. I. Ismail Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt, Egypt. Rasha M. Abd El-Aziz Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabia; Computer Science Department, Faculty of Computers and Information Assiut University, Egy Ahmed I. Taloba Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabia; Information System Department, Faculty of Computers and Information, Assiut University, The electrical reaction of bioactive sites in the individual’s body can be used to diagnose various disorders. Forecasts are made by examining the electric signal of the biologically active points onto patients. Measurements of the organ’s present level and variations in the passive electrical characteristics at specific bioactive sites on the body were made to evaluate the influence on the organ. The study aims to create a Neutrosophic fuzzy neural network (NFNN) approach to forecast the probability of complications following surgery. The research investigates a neural network method for predicting hazards associated with post-surgical care. Examining the current-voltage features of the biologically active spots forms the basis for the characteristics of the risk classifiers. By looking at patients who had been given a diagnosis of a disease, the training, as well as verification samples, as well as verification samples were created. Patients with type 1 had successful operations, but type 2 patients experienced a variety of post-operative problems, and type 3 patients needed extra treatment. The created classifiers show an excellent ability to foresee severe circumstances during surgical therapy. The neutrosophic fuzzy neural network model may be more sophisticated and advanced compared to conventional fuzzy neural network models. It can help differentiate the proposed model from existing models and highlight its unique features and advantages. The results show that the proposed 2023 232 239 1738 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.200421 https://www.americaspg.com/journal/21/article/1738 https://www.americaspg.com/storage/01683035457.pdf