IoT Based Health Monitoring System with AI-Powered Disease Prediction

Radha Mahendran, S. and Khan, Mohammad Shahbaz and M P, Hari Priya and Obaid, Ahmed J. and Mohsen, Karrar Shareef and Shareef, Ashraf Mohammed (2024) IoT Based Health Monitoring System with AI-Powered Disease Prediction. In: 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India.

Full text not available from this repository. (Request a copy)

Abstract

Healthcare monitoring systems have advanced significantly in emergency rooms and many other health settings. Today, many nations are deeply concerned about the rise of small healthcare monitoring systems. In-person advising and telemedicine are two different ways that the Internet of Things (IoT) is further developing healthcare. The Internet of Things makes things and individuals reliably available, and its relationship with the cloud improves our personal satisfaction. Proactive clinical exploration can help change a responsive healthcare approach into a proactive one, as man-made reasoning (AI) and high-level human insight become increasingly more coordinated into the healthcare area. Within the field of machine learning, deep learning holds significant potential in precisely and swiftly analysing vast quantities of data, producing perceptive experiences, and resolving intricate issues. Preventive treatment and early intervention for persons who are at risk depend on accurate and useful illness prediction. The constant inflow of electronic medical information necessitates the need for more accurate prediction models in recurrent neural network versions of deep learning that handle successive time-series data. Devices connected to the Internet of Things provide data to the proposed system, and future analysis will be conducted on cloud-stored electronic clinical records pertaining to patient histories.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computer Applications > Artificial Intelligence
Divisions: Bioinformatics
Depositing User: Mr IR Admin
Date Deposited: 06 Oct 2024 11:43
Last Modified: 06 Oct 2024 11:43
URI: https://ir.vistas.ac.in/id/eprint/9172

Actions (login required)

View Item
View Item