IoT based intelligent health surveillance & alert system with fault prediction using machine learning

Sharnil Pandya*, Rujul Desai, Darshita Pathak, Ketan Kotecha

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The primary target of this paper is to watch the patient wellbeing subtleties each and each second and update the little print to Server via a complicated IoT similarly as Care takers will straightaway read or monitor the current position of patients with none hidden activities. The intention is to realize high level of accuracy and speed. Net of Things (IoT) a boon to communication field, that connects the remote individuals via international medium. The most concern of IOT is to modify the powerful network association in little devices. During this system the concentration is comprise Medication system primarily based health care police work. This technique permits the patient details to be read to international server with reference to access management perspective. With this technique, nobody will cheat the care takers, nobody will hide the patient health outline and nobody having restriction to understand regarding the particular state of affairs of several patient. During this framework, a fresh out of the box new system is presented for auxiliary wellbeing viewing (SHM) exploitation IoT advances on astute and dependable viewing. In particular, advances worried in IoT and SHM framework usage are comparative as data steering system in IoT surroundings territory unit given. since the amount of data produced by detecting gadgets region unit voluminous and faster than at any other time, colossal data arrangements region unit acquainted with handle the progressed and massive amount of information gathered from sensors put in on structures. Prediction has important role for IOT. Those data which is sensed by sensors should be analyzed and should be predicted for some another condition. In our approach from past record we should identify the heart attack possibility to aware the patient. For that we have proposed machine learning approach with decision tree and logistic regression.

Original languageEnglish
Pages (from-to)660-676
Number of pages17
JournalInternational Journal of Advanced Science and Technology
Volume28
Issue number18
Publication statusPublished - 22 Dec 2019
Externally publishedYes

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