Volume 15 , Issue 2 , PP: 100-114, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Omar Ahmed Abdulkader 1 *
Doi: https://doi.org/10.54216/JCIM.150209
The cybersecurity and sustainability concepts involve safeguarding and analyzing sustainable systems, providing a versatile perspective. In the extensive data landscape of sustainable healthcare systems, ensuring diagnostic and security processes poses challenges. Healthcare disease detection using Blockchain (BC) employs BC technology to boost security and precision. This system securely shares and stores patient records through BC, fostering collaboration among researchers and healthcare providers to improve disease detection accuracy. This study designs a new BC-Assisted Al‐Biruni Earth Radius Optimization with Deep Learning Model for Sustainable Healthcare Disease Detection and Classification (BAERDL-SHDDC) technique. The BAERDL-SHDDC technique presented utilizes BC to securely store patient data and employs DL models to analyze the data for the disease detection process. For disease detection, the BAERDL-SHDDC technique involves a three-stage process namely Al‐Biruni Earth Radius (AER)-based feature selection, ensemble DL classification, and hyperparameter optimization. The hyperparameters of the ensemble DL models with fractals optimizations are optimally selected using an Adadelta optimizer. The stimulation result analysis of the BAERDL-SHDDC approach shows the guaranteeing performance of the BAERDL-SHDDC algorithm over other existing techniques with greater accuracy of 98.45%, 95.22%, and 96.49% under Heart Statlog, Pima Indian Diabetes, and EEG Eyestate databases respectively
Cybersecurity , Sustainability , Healthcare diagnosis , Blockchain , Security , Fractals Optimization , Deep learning
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