Real Time Health Monitoring Systems: A Review on Disease Prediction and Providing Medical Assistance to the Patients by the Data Mining Techniques and Cloud IoT

  • Ankit Verma, Gaurav Agarwal, Amit Kumar Gupta

Abstract

The Big data interest in healthcare system is massive role in the modern developing world due to the need of advance in the smart healthcare. Hence the health related data collections are increasingly in real time, which is linked with the electronic healthcare records (EHR) and others. IOT device enables the users to diminish the health related risks and healthcare costs by gather the patient’s data and analysis the data to allotment data stream using cloud computing. The patient parameters such as blood pressure, heart beat rate, glucose detection and temperature, etc are sensed by the particular sensors, based on the measured parameters  from the sensors is send to the cloud based method for an computerized disease prediction method. From this process, both the doctors and patients times are saved for the subsequent medical assessment rapidly given, moreover it is right to use at anytime and anyplace when it is necessary. The patient can get proper and efficient medical treatment by collecting data through monitor the current status of patient stored by the cloud using IOT. In this paper, the cloud IoT with various big data analytics linked in healthcares system and the different appropriate healthcare data analytics algorithm, tools and techniques are developed in the cloud Internet of Things (IoT) location are reviewed. At last the contribution is known in configuration of a convergence position of all these platforms in form of Smart Health that could result in contributing to unified standard learning healthcare system for future.

Published
2020-05-17
How to Cite
Ankit Verma, Gaurav Agarwal, Amit Kumar Gupta. (2020). Real Time Health Monitoring Systems: A Review on Disease Prediction and Providing Medical Assistance to the Patients by the Data Mining Techniques and Cloud IoT. International Journal of Control and Automation, 13(4), 162 - 180. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/16062
Section
Articles