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

• DOI: 10.3390/electronics10233013
• Publication Date: 2021-12-03T02:19:08Z
• Authors: Ankit Verma; Gaurav Agarwal; Amit Kumar Gupta; Mangal Sain
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 3. Good health

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