Novel Hybrid Intelligent Secure Cloud Internet of Things Based Disease Prediction and Diagnosis

Abstract

Nowadays, more people are affected by various diseases such as blood pressure, heart failure, etc. The early prediction of diseases tends to increase the survival of affected patients by allowing preventive action. A key element for this purpose is the digitalization of the healthcare system through the Internet of Things (IoT) and cloud computing. Nevertheless, there are major problems in the cloud with the IoT due to false predictions and errors in medical data, which results in taking a longer time to receive patient details and not providing the best outcome. Data transfer through the cloud can also be hacked by attackers due to the lack of security. This leads to a challenge for medical experts to predict the diseases accurately for a specific patient. Therefore, a novel hybrid elapid encryption (HEE) method was proposed for improving the security of cloud systems. In addition, the affected person’s disease and the severity risk level were predicted and classified using the proposed novel hybridization technique of the generalized-fuzzy-intelligence-based gray wolf ant lion optimization (GFI-GWALO) method. After the disease is predicted, the alert signal is provided to the patients. Moreover, this proposed research was implemented on MATLAB. Then the proposed simulation outcome was compared with various conventional methods and showed that the proposed method has the best outcomes in terms of its security and disease prediction with 80 ms of encryption time and 78 ms of decryption time, 100% accuracy, 99.50% precision and 8 ms of processing time.