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Title	Edge-Based Heart Disease Prediction Device Using Internet Of Things
ID_Doc	21821
Authors	Jenifer A.; Paulraj G.J.L.; Nithish Kumar K.; Yuvaraj T.; Alen G.; Peter Rozario F.; Amoli R.
Year	2022
Published	Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022
DOI	http://dx.doi.org/10.1109/ICAAIC53929.2022.9793104
Abstract	Internet of Things connects things for anywhere, anytime and anyone communication. It has wide applications that include healthcare, industry, transportation and smart cities. Internet of things enabled health applications have been developed in the past decade to measure vital signs viz., heart rate, oxygen level, accelerometer and ECG. These vital signs enable early detection of chronic diseases saving the life of people. However, many of the existing techniques aim at developing cloud-based processing for these devices. This increases the latency and consumes bandwidth for real-time critical applications. This also leaks the privacy of the patient's data. This paper aims at proposing an edge-based heart disease prediction device using the Internet of Things. The prototype was developed using raspberry pi connecting temperature, pulse rate and accelerometer sensor. The measured value is tested using a pre-trained machine learning model to understand the condition of the patient. The system was also tested under various cases and shows 88% of accuracy. © 2022 IEEE.
Author Keywords	Decision tree; Heart disease prediction; Internet of Things; Pulse sensor; Raspberry pi; Supervised machine learning algorithm

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