An industrial IoT-enabled smart healthcare system using big data mining and machine learning

The present spreading out in healthcare initiated with the appreciation of e-healthcare by interconnecting the medical sensors. IoT played a dynamic part in the healthcare industry, which founded the realization of the Industrial Internet of Things (IIoT). The IIoT is becoming a recognized measure of healthcare universally and is shifting the aptitude to treat patients. IIoT-enabled smart health can easily authenticate the patients and expedites efficient tracking. It supports critical information sharing for effective diagnosis and treatments. IIoT-enabled health systems are the combination of sensors, cameras, devices, and objects that produce a giant quantity of data called big data. The spreading out of Big Data founded the realization of Machine Learning (ML) to predict diseases for better treatment. The major goal of IIoT-enabled smart health systems is to investigate big data and provide valued insights for improved QoS. This research suggests an IIoT-enabled system for healthcare to process the Big Data generated by IIoT sensors using machine learning. The proposed scheme is the optimized parallel and distributed framework for real-time efficient processing. The proposed framework is comprised of three modules that are big data preparation, optimized model building, and big data computation. The data preparation is performed for accurate prediction and to get faster real-time big data processing. The current proposals lack effective prediction and resourceful parallel data processing. The proposed framework is simulated through an experiment with the parallel and distributed framework. The efficiency of the proposed scheme has been verified using authentic datasets with experimentations and simulations.