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  • Open Access

    ARTICLE

    Securing Mobile Cloud-Based Electronic Health Records: A Blockchain-Powered Cryptographic Solution with Enhanced Privacy and Efficiency

    Umer Nauman1, Yuhong Zhang2, Zhihui Li3, Tong Zhen1,3,*

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 15-34, 2024, DOI:10.32604/jimh.2024.048784 - 11 April 2024

    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 >

  • Open Access

    ARTICLE

    An Efficient Ensemble Model for Various Scale Medical Data

    Heba A. Elzeheiry*, Sherief Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1283-1305, 2022, DOI:10.32604/cmc.2022.027345 - 18 May 2022

    Abstract Electronic Health Records (EHRs) are the digital form of patients’ medical reports or records. EHRs facilitate advanced analytics and aid in better decision-making for clinical data. Medical data are very complicated and using one classification algorithm to reach good results is difficult. For this reason, we use a combination of classification techniques to reach an efficient and accurate classification model. This model combination is called the Ensemble model. We need to predict new medical data with a high accuracy value in a small processing time. We propose a new ensemble model MDRL which is efficient… More >

  • Open Access

    ARTICLE

    An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

    Adil Hussain Seh1, Jehad F. Al-Amri2, Ahmad F. Subahi3, Alka Agrawal1, Nitish Pathak4, Rajeev Kumar5,6,*, Raees Ahmad Khan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1387-1422, 2022, DOI:10.32604/cmes.2022.018163 - 30 December 2021

    Abstract The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’ data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital… More >

  • Open Access

    ARTICLE

    Machine Learning Techniques Applied to Electronic Healthcare Records to Predict Cancer Patient Survivability

    Ornela Bardhi1,2,*, Begonya Garcia Zapirain1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1595-1613, 2021, DOI:10.32604/cmc.2021.015326 - 13 April 2021

    Abstract Breast cancer (BCa) and prostate cancer (PCa) are the two most common types of cancer. Various factors play a role in these cancers, and discovering the most important ones might help patients live longer, better lives. This study aims to determine the variables that most affect patient survivability, and how the use of different machine learning algorithms can assist in such predictions. The AURIA database was used, which contains electronic healthcare records (EHRs) of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland. In total, there were 178… More >

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