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

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.

KEYWORDS:

• Enterprise health information system
• internet of things
• alcohol abuse
• healthcare
• machine learning
• fog computing
• IoT
• information system

Acknowledgments

We would like to thank Dr. Abrar Gani (NHS, UK) for his valuable comments, useful suggestions and discussion to improve the quality of the paper. We are grateful to the editors, area editor and anonymous reviewers for their invaluable comments and suggestions for improving the paper, which have greatly helped us to improve the paper. We also thank the participating anonymous employees of various enterprises who responded to the survey questionnaire and contributed significantly to the research.

Disclosure statement

No potential conflict of interest was reported by the authors.