IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

Arwinder Dhillon, Ashima Singh, Harpreet Vohra, Caroline Ellis, Blesson Varghese, Sukhpal Singh Gill

Research output: Contribution to journalArticle

Abstract

This paper proposes IoT-based an enterprise health information system called IoTPulse to predict alcohol addiction providing real-time data using machine-learning in fog computing environment. We used data from 300 alcohol addicts from Punjab (India) as a case study to train machine-learning models. The performance of IoTPulse is compared against existing work using various parameters including accuracy, sensitivity, specificity and precision which shows improvement of 7%, 4%, 12% and 12%, respectively. Finally, IoTPulse is validated in FogBus-based real fog environment using QoS parameters including latency, network bandwidth, energy and response time which improves performance by 19.56%, 18.36%, 19.53% and 21.56%, respectively.
Original languageEnglish
JournalEnterprise Information Systems
Early online date21 Sep 2020
DOIs
Publication statusEarly online date - 21 Sep 2020

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