Next Article in Journal
Robust Non-Parametric Mortality and Fertility Modelling and Forecasting: Gaussian Process Regression Approaches
Next Article in Special Issue
Fighting Deepfakes Using Body Language Analysis
Previous Article in Journal
Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes
Review

Trends in Using IoT with Machine Learning in Health Prediction System

1
Department of Cybersecurity, College of Computing Science and Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia
2
School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Received: 18 January 2021 / Revised: 21 February 2021 / Accepted: 3 March 2021 / Published: 7 March 2021
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends. View Full-Text
Keywords: IoT; ML; health prediction system; classification; prediction; supervised learning IoT; ML; health prediction system; classification; prediction; supervised learning
Show Figures

Figure 1

MDPI and ACS Style

Aldahiri, A.; Alrashed, B.; Hussain, W. Trends in Using IoT with Machine Learning in Health Prediction System. Forecasting 2021, 3, 181-206. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010012

AMA Style

Aldahiri A, Alrashed B, Hussain W. Trends in Using IoT with Machine Learning in Health Prediction System. Forecasting. 2021; 3(1):181-206. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010012

Chicago/Turabian Style

Aldahiri, Amani, Bashair Alrashed, and Walayat Hussain. 2021. "Trends in Using IoT with Machine Learning in Health Prediction System" Forecasting 3, no. 1: 181-206. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010012

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop