Predictive data analysis approach for securing medical data in smart grid healthcare systems

Modern healthcare systems (HS) rely on system-aided analysis and communication technology for providing reliable medical assistance for end-users. To improve data security features, healthcare and grid data are to be processed selectively to prevent illegal access to sensitive information. This study introduces the predictive data analysis (PDA) approach for HSs to prevent illegal access to medical data. In this PDA analysis, the different medical and grid data is analyzed to share information through the transfer learning function. The process of data matching is performed recurrently to classify the loss and predict the accurate analysis data. The intensive learning and training process of the proposed method differentiates authenticated and illegal access to healthcare data. The proposed method's performance is verified using the metrics accuracy, data loss, and processing time by varying the users and data size, respectively. (C) 2020 Elsevier B.V. All rights reserved.