What does this research mean for the field?

The proposed IoT-enabled system achieved 97.08% accuracy in detecting abnormal cardiac conditions using a decision tree classifier. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop an IoT-based system for real-time cardiac activity monitoring and abnormal condition alerts.

January 10, 2026Open Access

A Hybrid Early Warning System Based on IoT and Machine Learning for Heart Disease Prediction

Key Result

The proposed IoT-enabled system achieved 97.08% accuracy in detecting abnormal cardiac conditions using a decision tree classifier.

Key Points

The aim is to develop an IoT-based system for real-time cardiac activity monitoring and abnormal condition alerts.
Integration of multi-sensor data for physiological measurements
Application of machine learning classification techniques to analyze data
Real-time alerts sent to nursing staff via a mobile app
Evaluation using UCI benchmark datasets and real sensor measurements
Decision tree classifier achieved the highest accuracy of 97.08% in detecting abnormal cardiac conditions
System effectively identifies potential cardiac disorders using real-time data
Integration of emergency tracking enhances response during critical situations

Structured PICO

Population

Data from the UCI Machine Learning Repository heart disease dataset and real-time physiological data (pulse, blood pressure, temperature) collected from users across different age groups.

Intervention

Hybrid IoT-enabled early warning system integrating physiological sensors (pulse, blood pressure, temperature), an Arduino Uno microcontroller, GSM/GPS modules for real-time alerts, and a Decision Tree machine learning classifier.

Comparator

Other machine learning classification algorithms (Logistic Regression, k-Nearest Neighbors, Support Vector Machine).

Outcome

Prediction accuracy in distinguishing abnormal cardiac conditions.surrogate

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.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

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.

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