DNN-based Secure Remote Patient Data Analysis Framework for Improving Human Life Expectancy in Healthcare 4.0

Abstract

The evolution of healthcare from its early beginnings to the recent healthcare 4.0 revolution has remarkably improved human life and living standards. The integration of emerging technologies has played a pivotal role in this progress. Notably, remote analysis of patient data has emerged as a promising approach, enabling telemedicine and remote patient monitoring enhancing healthcare accessibility and efficiency. However, the vulnerabilities of patient data to malicious network attacks pose critical challenges in the healthcare industry, potentially compromising patients’ safety and undermining trust in the system. Thus, we have introduced a cutting-edge deep neural network (DNN) model to mitigate the risks associated with remote patient data transfer in healthcare 4.0 by bifurcating the data into malicious or non-malicious. The primary objective of the proposed framework is to ensure the secure and private communication of patient data, thereby fostering a more dependable and trustworthy healthcare ecosystem. Finally, the proposed framework is evaluated against various standard metrics such as accuracy, loss considering binary cross-entropy, receiver operating characteristic (ROC) curve, precision-recall curve and confusion matrix. The implications of the proposed framework offer data security to the healthcare system as it contributes to creating a more resilient and dependable ecosystem, thus promoting better patient care and ultimately elevating the overall life expectancy and well-being of individuals.