Optimizing IoT-Based Healthcare Systems with Scalable AI and Machine Learning Using Cloud Platforms

The connection of IoT-based healthcare systems with large-scale artificial intelligence and cloud platforms can be considered as radical in terms of prognosis and improvement of real-time patient attention. IoT technology in healthcare calls for an intelligent model in the cloud computing paradigm for efficient collection, processing, and analysis of the gathered data, which is provided by this paper’s framework. It is intended to improve the precision and speed of the applications in the field of predictive health care including with regard to heart rate, blood pressure, and oxygen saturation, as well as the growability and resource utilization of the system. The feasibility and efficiency of the proposed framework were discussed via specific simulations, indicating that the entire system can process great numbers of data with low latency and high predictive accuracy. The XGBoost model with a 93% prediction accuracy is suggested by results; intra-cloud resource consumption has been consistent. The utilization of AI models within cloud infrastructure also enabled effective real time data management. Thus, the challenges like data privacy, long time of data processing, high energy consumption were revealed but the system has a good potential for further developments of IoT based health care systems.