aspg-3-1687-1791943547 20261014020547 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2023 10 1 Sameer Kadem Dijlah University College,Baghdad, Iraq Noor Sami Department of computer engineering techniques, Mazaya University College, Th Qar, Iraq Ahmed Elaraby Department of Computer science, Faculty of computer science and information, South Valley University,Qena, Egypt; Department Of Cybersecurity, College of Engineering and Information Technolog Shahad Alyousif Department of Electrical and Electronic Engineering, College of Engineering, Gulf University, Sanad 26489, Kingdom of Bahrain; Research centre, Northampton university, faculty of engineering, Mohammed Jalil Department of computer science, Alturath University College, Baghdad, Iraq M. Altaee Department of medical engineering techniques, Alfarahidi University, Baghdad, Iraq Muntather Almusawi College of technical engineering, The Islamic University, Najaf, Iraq Ismaeel, A. Ghany Computer Technology Engineering, College of Engineering Technology, Al-Kitab University, Iraq Ali Kamil Kareem Department of Biomedical Engineering, Al-Mustaqbal University College, 51001 Hillah, Iraq Massila Kamalrudin Faculty of Information & Communication Technology, Universiti Teknikal Malaysia Melaka, 75450 Durian Tunggal, Melaka Adnan Allwi ftaiet Medical instruments engineering techniques, National University of science and technology, Thi Qar,Iraq The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data and Electroencephalography (EEG) measurements to predict outcomes over a two-year period. The study employs a multilevel fusion of data features to enhance the accuracy of the predictions. Therefore this paper suggests a hybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy brain injury (HCM-BI). A Support Vector Machine is applied with clinical details to define the Hypoxia-Ischemia outcomes of each infant. The newborn babies are assessed every two years again to know the neural development results. A selection of four attributes is derived from the Electroencephalography records, and SVM does not get conclusions regarding the classification of diseases. The final feature extraction of the EEG signal is optimized by the Bayesian Neural Network (BNN) to get the clear health condition of Hypoglycemia and Epilepsy patients. Through monitoring and assessing physical effects resulting from Electroencephalography, The Bayesian Neural Network (BNN) is used to extract the test samples with the most log data and to report hypoglycemia and epilepsy patients non-invasively. The experimental findings demonstrate that the suggested strategy improves accuracy by 95.05% and reduces the error rate to 0.41 when comparing diseases. 2023 100 115 1687 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.100106 https://americaspg.com/journal/3/article/1687 https://americaspg.com/storage/41681396880.pdf