Smart Heart Disease Prediction and Amalgamation Tracking System

Ashok Kumar Shanmugaraj; Arunkumar Nagaradjane; Anandkumar Candassamy; Karthikcharan Dhamodharan

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

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