Machine Inspired IOT based Framework for Real-Time Heart Disease Prediction

Abstract

The rapid advancements in Internet of Things (IoT) technologies have enabled the development of innovative healthcare solutions, particularly in the field of real-time disease prediction and management. This paper presents a machine-inspired IoT-based framework designed for the real-time prediction of heart disease. The proposed framework integrates IoT-enabled wearable devices that continuously monitor vital signs such as heart rate, blood pressure, and oxygen saturation. These devices transmit data to a central processing unit where machine learning algorithms analyze the information to detect early signs of heart disease. By leveraging real-time data and advanced predictive models, the framework aims to provide timely alerts to healthcare providers and patients, thereby facilitating early intervention and reducing the risk of severe cardiac events. The framework's architecture is built on a robust and scalable IoT infrastructure that ensures seamless data collection, transmission, and analysis. Machine learning techniques, including supervised learning models and ensemble methods, are employed to enhance the accuracy of heart disease predictions. The system also incorporates edge computing to reduce latency and improve processing efficiency, enabling real-time analysis even in resource-constrained environments. Experimental results demonstrate the framework's potential in achieving high predictive accuracy while maintaining low power consumption, making it a viable solution for continuous heart health monitoring. This work contributes to the growing field of smart healthcare by offering a practical and efficient approach to real-time heart disease prediction, ultimately aiming to improve patient outcomes through proactive healthcare management.