Title: Innovative edge computing-driven recognition of cardiac monitoring application

Authors: R. Kishore Kanna; Rajendar Sandiri; Biswajit Brahma; Bhawani Sankar Panigrahi; Susanta Kumar Sahoo; Rajitha Ala

Addresses: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology, Chennai, India ' Vardhaman College of Engineering, Hyderabad, Telangana, India ' McKesson Corporation, Irving, Texas, USA ' GITAM Institute of Technology, GITAM University, Vishakhapatnam, India ' Indira Gandhi Institute of Technology, Sarang (Autonomous Institute of Govt. Of Odisha) Dhenkanal, Odisha, India ' Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, India

Abstract: Cardiac disease has been emerged as a significant global health concern. The mortality rate for abrupt cardiac arrest is high. Continuous monitoring of subject's physical condition has the potential to save up to 60% of human lives. The IoT executes well in the remote monitoring of a patient's status. The widespread utilisation of wearable devices will enhance the impact of the Internet of Things (IoT). This study evaluates five prediction models for specific data and selects the random forest algorithm as the best method in edge computing applications. Suggests MAX30102 for strong plan is equivalent to patient's assessment, their remains patient vitals and parameters and they are the matrix. The user authentication will be loaded on the database. The selected predictive modelling approach analyses critical information for any anomalies and alert messages based on abnormality data detection in heartbeat are transmitted via ThingSpeak cloud to the nearest healthcare facility.

Keywords: computing; edge-computing; cardiac; healthcare.

DOI: 10.1504/IJGUC.2025.148537

International Journal of Grid and Utility Computing, 2025 Vol.16 No.5/6, pp.552 - 560

Received: 24 May 2024
Accepted: 18 Jul 2024
Published online: 11 Sep 2025 *

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