Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

Abstract

In healthcare, the mixture of AI and IoT has unique opportunities to improve sickness analysis by using analysing information in actual-time, create individualized remedy regimens, and boom patient pleasure. Collaborative model training is possible, however protecting patients' personal information is a significant hurdle that demands for new approaches. Data privacy and security, IoT device compatibility, and regulatory compliance are major obstacles to constructing AI and IoT-enabled illness diagnostic models (IoT-IDM). Thoroughly navigating those challenges is important for figuring out the ability for synthetic intelligence and the internet of things to revolutionize healthcare shipping. A potential solution to the safety and privacy issues related to collaborative model schooling in decentralized healthcare settings is provided in this paper as Federated Learning with Blockchain-Based Privacy Preservation Techniques (FLB-PPT). Utilizing blockchain technology and federated learning principles, FLB-PPT permits widespread IoT device collaboration during model training while encrypting, differentially protecting, and decentralizing patient data to ensure privacy. Remote patient monitoring, early infection detection, chronic ailment management, predictive analytics, and the Internet of Things (IoT) are many of the few the healthcare sectors that may benefit from the cautioned sickness diagnostic version that is superior to FLB-PPT. Healthcare practitioners may empower themselves to give more accurate and tailored treatment with FLB-PPT, which harnesses the collective intelligence of distributed IoT devices while ensuring patient privacy. Evaluation of the FLB-PPT framework's performance and efficacy in a simulated healthcare environment is achieved through simulation analysis. The practicality and efficacy of FLB-PPT in actual healthcare settings are shown by conducting thorough simulations and evaluating critical parameters including model correctness, convergence rate, communication overhead, and privacy protection.