In the modern era, wireless devices have entangled and influenced the medical space on a large scale of effectiveness. To monitor an elderly person who need medical attention in the periodic interval is very vapid using existing technologies. To conquer this, we select a unique wearable sensor, so it can be remote monitoring and data gathering of patients on the cloud through the internet of things which adds the advantages of mobility. Thus around the clock medical attention can be given to the patient by rigorous data observation before medical condition to escalate. In this work, all the viable options regarding patient surveillance are considered. SVM is a supervised learning algorithm.In this work,there is a need to manually keep the label of the standard value of parameter data with what the range predicts the correct choice for the train an SVM model as the need for classification. Eventually, it can be used for optimal prediction algorithm. The strategy of this work is done with the help of frontier technology, IoT and machine learning. This research work addresses the challenge in computing to optimize the efficiency of prediction data with the IoT-enabled information architecture-driven approach.
Digital Object Identifier (DOI)
Sanjeev Kumar, Neelam and Nirmalkumar, P.
"An Intelligent Decision-Support System For Telemedicine,"
Applied Mathematics & Information Sciences: Vol. 12:
5, Article 11.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol12/iss5/11