IoT eHealth Big Data Analytics Based on Machine Learning

The convergence of health care and technology has led to the emergence of numerous inventive solutions, among which the Internet of Things (IoT) in tandem with Big Data Analytics and Machine Learning appears to hold significant potential. In the previous research that has been conducted on the Internet of Things (IoT), a variety of big data solutions has been presented for the purpose of evaluating massive amounts of data that have been obtained from a wide variety of sources in the field of smart health care. This evaluation is intended to take place to provide better care for the patients. This has been done with the goal of enhancing the quality of medical treatment offered to patients. Machine learning (ML) is a cutting-edge technology in the industry that has the capacity to analyze huge amounts of data and come to judgements based on those analyses in unique ways. This ability makes ML one of the most important developments in this field in recent years. In this article, we propose to use three distinct machine learning algorithms, each of which serves as a learning engine for big data analytics. Our goal is to investigate how quickly data is processed and how well it can be used to make predictions. Python is used throughout the whole training and testing process, including the simulation that is used to determine whether the model is effective. The findings of the simulation indicate that the suggested technique has a high degree of accuracy in determining the medical histories of patients based on the data provided by IoT devices.