A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

Hemantha Krishna Bharadwaj,Biplab Sikdar,Vinay Chamola,Naga Rajiv Lakkaniga,Mohsen Guizani,Vikas Hassija,Aayush Agarwal

doi:10.1109/access.2021.3059858

Abstract

The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.

Highlights

The Internet of Things (IoT) has been the subject of great enthusiasm in the healthcare technology community over the last few years
Various contemporary research efforts are aimed at finding out new areas of applications of machine learning (ML) algorithms to Healthcare Internet of Things (H-IoT) systems, evaluating their suitability for these systems, and increasing the accuracy achieved by prediction and analysis models
An H-IoT system comprises of an end-to-end network typically consisting of three major layers of operation [16]: 1) Data collection layer: This layer is responsible for the collection of medical data from various sensor devices attached to the patient/test subject that needs to be monitored/examined

Summary

INTRODUCTION

The Internet of Things (IoT) has been the subject of great enthusiasm in the healthcare technology community over the last few years. DEFINITION OF H-IOT A healthcare IoT system can be defined as a network of all available health resources connected to each other for rapid transfer of data between them over the Internet [11] This means that all healthcare resources like doctors, hospitals, rehabilitation centres and all medical devices and sensors along with the patients become interconnected with each other for continuous real-time data transfer. The various sensors coupled with applications that interpret their readings can detect anomalies and send patient data to medical practitioners/hospitals for diagnosis and analysis, after which corrective action can be prescribed and undertaken For such a framework to exist and work smoothly, three primary requirements need to be met [12]: VOLUME 9, 2021. Various mechanisms for authorization and authentication of IoT devices are available using technologies such as encryption and physical unclonable functions [13]–[15]

ARCHITECTURE OF H-IOT

LINEAR REGRESSION

SUPPORT VECTOR MACHINE

ASSISTIVE SYSTEMS

Findings

CONCLUSION