Smart Heart Disease Prediction and Amalgamation Tracking System

Authors

  • Ashok Kumar Shanmugaraj Department of Electronics and Communication Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India -605107
  • Arunkumar Nagaradjane Department of Electronics and Communication Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India -605107
  • Anandkumar Candassamy Department of Electronics and Communication Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India -605107
  • Karthikcharan Dhamodharan Department of Electronics and Communication Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India -605107

Abstract

Cardiovascular disease is the foremost reason for death globally in recent times. In order to prevent such losses, heart disease prediction and tracking system that is reliable, accurate, scalable, and cost-effective is essential. This work proposes a system that utilizes an artificial intelligence processor (LSAI48266X) and an IoT device to transfer data from sensors such as the Mach30100 and DS18B20. The system aims to track, visualize, and forecast heart disease. Random Forest is a machine learning algorithm that predicts cardiac illness based on numerous parameters such as SpO2, heartbeat, temperature, and blood pressure. Web application is developed using PHP that displays hospital details and integrated with a Telegram chatbot for communication during emergency conditions. In comparison to previous works, the proposed system has a high degree of accuracy of 95.6% and automation system to prevent loss of human life by incorporating the Random Forest Algorithm and tracking system.

Additional Files

Published

2024-05-20

Issue

Engineering Review, Vol. 44, No. 1

Section

Articles

License

Copyright (c) 2024 Engineering Review

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Engineering review uses the Creative Commons Attribution-NonCommercial-NoDerivatives (CC-BY-NC-ND) 4.0 International License, which governs the use, publishing and distribution of articles by authors, publishers and the wider general public.

The authors are allowed to post a digital file of the published article, or the link to the published article (Enginering Review web page) may be made publicly available on websites or repositories, such as the Author’s personal website, preprint servers, university networks or primary employer’s institutional websites, third party institutional or subject-based repositories, and conference websites that feature presentations by the Author(s) based on the published article, under the condition that the article is posted in its unaltered Engineering Review form, exclusively for non-commercial purposes.

The journal Engineering Review’s publishing procedure is performed in accordance with the publishing ethics statements, defined within the Publishing Ethics Resource Kit. The Ethics statement is available in the document Ethics Policies.