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Uma Ramasamy^*, Sundar Santhoshkumar
Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 1-14, 2024, DOI:10.32604/jimh.2023.046995
Abstract More than 140 autoimmune diseases have distinct autoantibodies and symptoms, and it makes it challenging to construct an appropriate model using Machine Learning (ML) for autoimmune disease. Arthritis-related autoimmunity requires special attention. Although many conventional biomarkers for arthritis have been established, more biomarkers of arthritis autoimmune diseases remain to be identified. This review focuses on the research conducted using data obtained from clinical laboratory testing of real-time arthritis patients. The collected data is labelled the Arthritis Profile Data (APD) dataset. The APD dataset is the retrospective data with many missing values. We undertook a comprehensive… More >

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Umer Nauman¹, Yuhong Zhang², Zhihui Li³, Tong Zhen^1,3,*
Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 15-34, 2024, DOI:10.32604/jimh.2024.048784
Abstract The convergence of handheld devices and cloud-based computing has transformed how Electronic Health Records (EHRs) are stored in mobile cloud paradigms, offering benefits such as affordability, adaptability, and portability. However, it also introduces challenges regarding network security and data confidentiality, as it aims to exchange EHRs among mobile users while maintaining high levels of security. This study proposes an innovative blockchain-based solution to these issues and presents secure cloud storage for healthcare data. To provide enhanced cryptography, the proposed method combines an enhanced Blowfish encryption method with a new key generation technique called Elephant Herding… More >

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Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶
Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340
Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

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