IoT based Smart Healthcare System in Cloud Environment

People are increasingly concerned about their health and need to keep it properly. The rapidly growing human population needs sophisticated systems to forecast patients' health status and appropriate treatment. The newest technological breakthroughs and innovations assist the healthcare business overcome prediction challenges. The Internet of Things (IoT) and Deep Learning technologies help transport health-related data from the local entity to the server and preserve it after evaluation. These regulations allow the medical sector to develop new health prediction technologies while saving countless lives. Parallel to this, these characteristics attract various hackers or invaders to get health data and abuse it. So, the main worry of internet-based health record monitoring and analysis is security. This research introduces a new deep learning technique called Learning Assisted Secured Health Prediction (LASHP), developed from the traditional learning model called Artificial Neural Network (ANN). This technique combines Deep Learning, Cryptographic Security Mechanisms, and the Internet of Things to create a durable model. IoT logic transports data securely from local units to remote servers for processing. The suggested healthcare prediction method is secured using the Modified Cipher Scheme (MCS), linked with the proposed LASHP logic. Thus, the suggested Learning Assisted Secured Health Prediction technique is efficient and effective in the next portion of this work