Volume 20 , Issue 4 , PP: 232-239, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammad Kanan 1 * , Nadir Omer 2 , Safaa S. I. Ismail 3 , Rasha M. Abd El-Aziz 4 , Ahmed I. Taloba 5
Doi: https://doi.org/10.54216/IJNS.200421
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
Fuzzy neural network , neutrosophic sets , biologically active points , neutrosophic fuzzy neural network
[1] Mohamed Abdel-Basset, Abduallah Gamal, Gunasekaran Manogaran, Le Hoang Son, and Hoang Viet Long. A novel group decision making model based on neutrosophic sets for heart disease diagnosis. Multimedia Tools and Applications, 79:9977–10002, 2020.
[2] Amr Abozeid, Rayan Alanazi, Ahmed Elhadad, Ahmed I Taloba, Abd El-Aziz, and M Rasha. A largescale dataset and deep learning model for detecting and counting olive trees in satellite imagery. Computational Intelligence and Neuroscience, 2022, 2022.
[3] Fekadu Tesgera Agama and VN SrinivasaRao Repalle. A study on an extended total fuzzy graph. Appl. Math, 16(4):511–518, 2022.
[4] Faisal Al-Sharqi and Ashraf Al-Quran Abd Ghafur Ahmad. Mapping on interval complex neutrosophic soft sets. International Journal of Neutrosophic Science, 19(4):77–85, 2022.
[5] Majed G Alharbi and Hamiden Abd El-Wahed Khalifa. On solutions of fully fuzzy linear fractional programming problems using close interval approximation for normalized heptagonal fuzzy numbers. Appl. Math, 15(4):471–477, 2021.
[6] Elham Babaie. A novel method for software reliability assessment via neuro-fuzzy system. International Journal of Reliability, Risk and Safety: Theory and Application, 5(1):43–48, 2022.
[7] Vincent Bourbonne, Martin Valli`eres, Franc¸ois Lucia, Laurent Doucet, Dimitris Visvikis, Valentin Tissot, Olivier Pradier, Mathieu Hatt, and Ulrike Schick. Mri-derived radiomics to guide post-operative management for high-risk prostate cancer. Frontiers in oncology, 9:807, 2019.
[8] Qian Chen, Chao Wang, Xudong Zhang, Guojun Chen, Quanyin Hu, Hongjun Li, Jinqiang Wang, Di Wen, Yuqi Zhang, Yifei Lu, et al. In situ sprayed bioresponsive immunotherapeutic gel for postsurgical cancer treatment. Nature nanotechnology, 14(1):89–97, 2019.
[9] Paulo Vitor de Campos Souza. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Applied soft computing, 92:106275, 2020.
[10] Diego de Miguel-P´erez, Clara Isabel Bayarri-Lara, Francisco Gabriel Ortega, Alessandro Russo, Mar´ıa Jos´e Moyano Rodriguez, Maria Jesus Alvarez-Cubero, Elizabeth Maza Serrano, Jos´e Antonio Lorente, Christian Rolfo, and Mar´ıa Jos´e Serrano. Post-surgery circulating tumor cells and axl overexpression as new poor prognostic biomarkers in resected lung adenocarcinoma. Cancers, 11(11):1750, 2019.
[11] Bayram Ersoy, Serkan Onar, Kostaq Hila, and Krisanthi Naka. (t, s)-intuitionistic fuzzy hyperideals of γ-hyperrings. Applied Mathematics & Information Sciences, 15(3), 2021.
[12] Sergey Filist, Riad Taha Al-Kasasbeh, Olga Shatalova, Nikolay Korenevskiy, Ashraf Shaqadan, Zeinab Protasova, Maksim Ilyash, and Mikhail Lukashov. Biotechnical system based on fuzzy logic prediction for surgical risk classification using analysis of current-voltage characteristics of acupuncture points. Journal of Integrative Medicine, 20(3):252–264, 2022.
[13] MFrager, N Cnossen, and SM Shin. Clinical outcomes of radical prostatectomy versus combined external beam radiation therapy and androgen deprivation therapy in elderly men with high-risk prostate cancer: a multi-institutional analysis. International Journal of Radiation Oncology, Biology, Physics, 102(3):e110, 2018.
[14] Matthew D Giglia and Sharon L Stein. Overlooked long-term complications of colorectal surgery. Clinics in Colon and Rectal Surgery, 32(03):204–211, 2019.
[15] Wael K Hanna and Nouran M Radwan. Heart disease patient risk classification based on neutrosophic sets. International Journal of Business Intelligence and Data Mining, 20(1):93–106, 2022.
[16] Atimad Harir, Said Melliani, and L Saadia Chadli. Convergence of fuzzy conformable laplace transform. Appl. Math, 16(6):863–870, 2022.
[17] EA Katsareli, C Amerikanou, K Rouskas, A Dimopoulos, T Diamantis, A Alexandrou, J Griniatsos, S Bourgeois, E Dermitzakis, J Ragoussis, et al. A genetic risk score for the estimation of weight loss after bariatric surgery. Obesity surgery, 30:1482–1490, 2020.
[18] HA Khalifa, Pavan Kumar, and Bayoumi Ali Hassan. An inexact rough interval of normalized heptagonal fuzzy numbers for solving vendor selection problem. Appl. Math, 15(3):317–324, 2021.
[19] AM Kozae, Mohamed Shokry, and Manar Omran. Comparison between fuzzy soft expert system and intuitionistic fuzzy set in prediction of luge cancer. Information Sciences Letters, 10(2):167–176, 2021.
[20] Ellen CD K¨uhn-Heid, Eike C K¨uhn, Julia Ney, Sebastian Wendt, Julian Seelig, Christian Schwiebert, Waldemar B Minich, Christian Stoppe, and Lutz Schomburg. Selenium-binding protein 1 indicates myocardial stress and risk for adverse outcome in cardiac surgery. Nutrients, 11(9):2005, 2019.
[21] M Lashin and A Malibari. Using fuzzy logic control system as an artificial intelligence tool to design soap bubbles robot as a type of interactive games. Inf. Sci. Lett, 11:15–19, 2022.
[22] Jean-Michel Lem´ee, Marco V Corniola, Michele Da Broi, Karl Schaller, and Torstein R Meling. Early postoperative complications in meningioma: predictive factors and impact on outcome. World neurosurgery, 128:e851–e858, 2019.
[23] MLeyva, P Del Pozo, and A Pe˜nafiel. Neutrosophic dematel in the analysis of the causal factors of youth violence. International Journal of Neutrosophic Science, 18(3):199–207, 2022.
[24] Mushtaq A Lone, SA Mir, Hilal MY Al-Bayatti, O O¨ zer, OF Khan, and T Mushtaq. Optimal allocation in agriculture using intuitionistic fuzzy assignment problem. Information Sciences Letters, 2021.
[25] Rasha M Abd El-Aziz, Rayan Alanazi, Osama R Shahin, Ahmed Elhadad, Amr Abozeid, Ahmed I Taloba, and Riyad Alshalabi. An effective data science technique for iot-assisted healthcare monitoring system with a rapid adoption of cloud computing. Computational Intelligence and Neuroscience,
2022, 2022.
[26] Ajjaz Maqbool, Chitranjan Sharma, Mushtaq A Lone, and Riyad Alshalabi. Intuitionistic fuzzy programming technique to solve multi-objective transportation problem. Res Rev J Stat Math Sci, 7(6):1–9, 2021.
[27] Khalida Inayat Noor. Fuzzy differential subordination involving generalized noor-salagean operator. Inf. Sci. Lett, 11:1–7, 2022.
[28] Khalida Inayat Noor. Fuzzy differential subordination involving generalized noor-salagean operator. Inf. Sci. Lett, 11:1–7, 2022.
[29] Juhyun Park, Dong Hyun Yoon, Sangjun Yoo, Sung Yong Cho, Min Chul Cho, Ga-Young Han, Wook Song, and Hyeon Jeong. Effects of progressive resistance training on post-surgery incontinence in men with prostate cancer. Journal of clinical medicine, 7(9):292, 2018.
[30] Carina Riediger, Tibor Schuster, Ulrich Bork, Johannes Schweipert, Maike Sigg, Juliane Weiss, and J¨urgen Weitz. Do certain surgical steps increase postoperative morbidity after cytoreductive surgery and hipec-a retrospective analysis. Surgical Oncology, 45:101884, 2022.
[31] Brittany N Rosenbloom, P Maxwell Slepian, M Gabrielle Pag´e, Lisa Isaac, Fiona Campbell, Jennifer Stinson, and Joel Katz. Differential risk factor profiles in the prediction of general and pain-specific functional limitations 12 months after major pediatric surgery. Children, 8(5):360, 2021.
[32] S Saleh, Radwan Abu-Gdairi, Tareq M Al-shami, and Mohammed S Abdo. On categorical property of fuzzy soft topological spaces. Appl. Math. Inform. Sci, 16:635–641, 2022.
[33] A Shaqadan and M Alrawashdeh. Prediction of concrete mix compressive strength using statistical learning models. J Eng Sci Technol, 13(7):1916–1925, 2018.
[34] Olga Shatalova, Sergey Filist, Nikolay Korenevskiy, Riad Taha Al-kasasbeh, Ashraf Shaqadan, Zeinab Protasova, Maksim Ilyash, and Anatoly Rybochkin. Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks. Computer Methods in Biomechanics and Biomedical Engineering, 24(13):1504–1516, 2021.
