Volume 14 No 2 (2022): International Journal of Early Childhood Special Education

IoT Framework with Support Vector Machine Learning Algorithm for Intelligent Health Monitoring System


Dr.Syed Khasim, Dr.Shaik Shakeer Basha

DOI: 10.9756/INT-JECSE/V14I2.193

Keywords: IoT, Decision Tree, Support Vector Machine, Machine Learning, Prediction


Abstract

The main purpose of this paper is to look at the patient's health crises every second and to update the subtitle on a server with sophisticated IoT in the same way as caregivers will learn or monitor the current state of patients with no hidden activity. The aim is to determine the highest level of accuracy and speed. Net of Things (IoT) is an advantage in the field of communication, which connects people far and wide through an international medium. The main concern of IoT is to adjust for strong network connectivity on small devices. During this program torture includes a Medicare program primarily based on the work of the police health care system. This process allows patient information to be read to an international server with a view to managing access. In this way, no one will cheat caregivers, no one will hide the patient's health outline and no one is limited in understanding about a particular patient's condition. During this framework, a new system emerges from the axillaries wellbeing viewing (SHM) IoT development for smart and reliable viewing. In particular, the worrying development in the use of the IoT framework and SHM is compared as a data management system in a given IoT environment unit. as the amount of data generated by obtaining a gadget regional unit is stronger and faster than ever, the regional unit of large data setting is equipped with an advanced and large amount of data collected on sensors embedded in structures. Predictability plays an important role for IoT. That sensory data should be analyzed and should be predicted in another context. In the case of past records, the risk of a heart attack should be identified so that the patient can be identified. In the proposed machine learning method with a decision tree and a decrease in performance.

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Volume 14 No 2 (2022): International Journal of Early Childhood Special Education

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Research Articles

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