Integrating IoT and ML in Smart Healthcare System: Comprehensive Approach

Automation in the field of health care is highly supported by the Internet of Things (IoT) in terms of patient outcomes and remote monitoring. This integration has proliferated in recent years, and by connecting these devices to a network we can perform distributed data analysis for medical purposes. An effective technique for extracting insights from IoT statistics is machine learning. The healthcare industry’s decision-making process is intelligently enhanced by these hybrid technologies. This helps in the identification and evaluation of parameters in relation to the disease and in the planning of treatment for patients. For the best prediction and recommendation systems, ML enables IoT to uncover hidden patterns in large amounts of data so that the outcome of patients will increase. This paper aims to provide thorough review of current machine learning techniques and how they are applied to medical data from the IoT. By discussing the role of IoT and ML in smart healthcare, we identified importance factors and their associated components that are responsible for their widespread use. In addition to this, a comparative analysis has also been proposed with the details of the disease, the combination of IOT sensor, and ML algorithm used with their accuracy.