Wearable Device Based on IoT in the Healthcare System for Disease Detection and Symptom Recognition

Pradhan, M R, Ateeq, K and Mago, B (2021) Wearable Device Based on IoT in the Healthcare System for Disease Detection and Symptom Recognition. International Journal on Artificial Intelligence Tools, 30 (06n08). ISSN 0218-2130

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Humans in good shape face many challenges in their lives, such as food habits and climate change. The result must be aware of the health situation to survive. Lack of accurate patient information, preventive errors, data risks, overdiagnosis, and delayed implementation are challenges that health support services face. Wearable sensors that connect extensive data, data mining analysis for healthcare, and the Internet of things (IoT) have been proposed to solve this problem. This research, Disease Prediction and Symptom Recognition Model using IoT (DDSR-IoT) framework, is proposed for reasoning with regression rules to gather patient information. The Boltzmann network to train Artificial Intelligence (AI) feedback is introduced in the end. As a result, the broad interaction analysis of genomes is used to predict conditions. If those infections have affected people, emails are sent to warn them and provide them with prescriptions and medical advice. In the recommended approach, the experimental study resulted in an enhanced forecast rate of 97.4 percent and a precision of 97.42 percent.

Affiliation: Skyline University College
SUC Author(s): Pradhan, M R ORCID: https://orcid.org/0000-0002-0115-2722, Ateeq, K ORCID: https://orcid.org/0000-0002-6712-6623 and Mago, B ORCID: https://orcid.org/0000-0003-1537-1202
All Author(s): Pradhan, M R, Ateeq, K and Mago, B
Item Type: Article
Uncontrolled Keywords: Artificial Intelligence (AI), Internet of Things (IoT), health sector, wearable devices
Subjects: B Information Technology > BM Artificial Intelligence
B Information Technology > BP Internet of Things
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 12 May 2022 08:17
Last Modified: 12 May 2022 08:17
URI: https://research.skylineuniversity.ac.ae/id/eprint/172
Publisher URL: https://doi.org/10.1142/S021821302140011X
Publisher OA policy: https://v2.sherpa.ac.uk/id/publication/9713
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