E-Healthcare Monitoring system using IoT and Machine Learning
Author(s):
Tushar Patil, Nisarga Pund, Yuvraj Palakudtewar, Shashank Biradar
Keywords:
Internet of Things, Machine Learning, Healthcare.
Abstract
The Internet of Things (IoT) is a new technology that is rapidly advancing in the health arena with numerous new developments. IoT devices are evolving with cutting-edge resources and technology to meet new problems. The health status of in- and out-patients can be routinely and periodically monitored with IoT devices. The field of machine learning is rapidly advancing and has the potential to completely transform the healthcare system in several ways, including patient care, treatment, and diagnosis. In order to create an advanced automation system, we concentrate on developing an IoT application framework for the Healthcare Monitoring System that is coupled with machine learning (ML) approaches to handle healthcare problems. This system will link, monitor, and make decisions for an accurate patient diagnosis. To validate and authenticate our proposed work, we obtained patient data from IoT devices and applied Machine Learning for analyse and prediction. We developed User Interface for patient and doctor communication The Internet of Things (IoT) is a new technology that is rapidly advancing in the health arena with numerous new developments. IoT devices are evolving with cutting-edge resources and technology to meet new problems. The health status of in- and out-patients can be routinely and periodically monitored with IoT devices. The field of machine learning is rapidly advancing and has the potential to completely transform the healthcare system in several ways, including patient care, treatment, and diagnosis. In order to create an advanced automation system, we concentrate on developing an IoT application framework for the Healthcare Monitoring System that is coupled with machine learning (ML) approaches to handle healthcare problems. This system will link, monitor, and make decisions for an accurate patient diagnosis. To validate and authenticate our proposed work, we obtained patient data from IoT devices and applied Machine Learning for analyse and prediction. We developed User Interface for patient and doctor communication.
Article Details
Unique Paper ID: 165017

Publication Volume & Issue: Volume 11, Issue 1

Page(s): 8 - 13
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