The proposed IoT-enabled system achieved 97.08% accuracy in detecting abnormal cardiac conditions using a decision tree classifier.
A hybrid IoT and machine learning system using a Decision Tree classifier achieved 97.08% accuracy in detecting abnormal cardiac parameters and can automatically send real-time GPS-enabled emergency alerts.
Absolute Event Rate: 0% vs 0%
The rapid development of the Internet of Things (IoT), enabled by the integration of sensors, embedded electronics, and networked software, has created new opportunities for real-time health monitoring applications. In the medical domain, IoT-based systems have gained significant attention for their ability to continuously acquire and transmit physiological parameters. This paper proposes an IoT-enabled early warning system for monitoring cardiac activity and generating alerts when abnormal heart conditions are detected. The proposed system is designed to support nursing staff by automatically notifying responsible personnel through a mobile application in the event of abnormal heart rate patterns. Data collected from multi-sensor physiological measurements are analyzed using several machine learning classification techniques to identify potential cardiac disorders. Experimental results indicate that the decision tree classifier achieves the highest accuracy of 97.08% in distinguishing abnormal cardiac conditions. Unlike existing IoT-based heart monitoring solutions, the proposed model integrates multi-sensor data acquisition, real-time GPS-based emergency tracking, and a multi-model machine learning evaluation framework validated using both UCI benchmark datasets and real sensor measurements. The results demonstrate the effectiveness of the proposed system for accurate early detection of cardiac abnormalities in clinical monitoring environments.
Chakraborty et al. (Mon,) reported a other. The proposed IoT-enabled system achieved 97.08% accuracy in detecting abnormal cardiac conditions using a decision tree classifier.