Harnessing the Power of Swarm Intelligence and Long Short-Term Memory Networks in Wearable Healthcare Systems

Current developments in cloud services, artificial intelligence (AI), and the Internet of Things (IoT) have transformed the traditional healthcare system into smart healthcare. Improved medical services can be achieved by utilising important technologies like AI and IoT. The current study article offers a novel illness detection model for smart healthcare systems that is based on the convergence of AI and IoT. The primary goal of this article is to develop a disease detection model for heart disease by combining data from smart wearable devices with convergence methodologies in artificial intelligence. The model under discussion involves many stages, such as gathering data, pre-processing, categorization, and adjusting parameters. Wearable’s and other Internet of Things (IoT) devices facilitate the easy collecting of data, which is then used by artificial intelligence (AI) algorithms to diagnose diseases. For illness diagnosis, the suggested approach makes use of the Swarm Intelligence-Powered Long Short-Term Memory (SILSTM) model. Healthcare data was used to validate the SILSTM model's performance. The research revealed that the SILSTM model that was provided could diagnose cardiac disease with a maximum accuracy of 96.16%. For this reason, smart healthcare systems can make use of the suggested SILSTM model as a suitable instrument for disease diagnosis.