Key result
IoT-based Decision Tree algorithm achieves ~85% accuracy for classifying patient vital parameters.
Why the study?
Heart disease is a leading cause of death worldwide, and real-time monitoring with IoT devices may enable early detection and classification of patient conditions.
Does an IoT-based Decision Tree algorithm accurately classify patient conditions based on vital parameters for the early detection of heart disease?
Does an IoT-based Decision Tree algorithm accurately classify patient conditions based on vital parameters for the early detection of heart disease?
Answers are grounded in this paper and cite their sources.
An IoT-based monitoring system using a Decision Tree algorithm achieved 85% accuracy in classifying vital parameters, demonstrating potential for early heart disease detection despite challenges with class imbalance.
Heart disease is one of the leading causes of death worldwide, accounting for 32% of all global deaths. Technological developments, particularly in the Internet of Things (IoT), enable real-time monitoring of heart health and early warning alerts. This study aims to implement a Decision Tree algorithm to classify patient conditions based on vital parameters, including BPM, SpO₂, systolic and diastolic blood pressure, and body temperature. The model was trained using a vital parameter dataset and evaluated using a confusion matrix, ROC curve, and feature importance. Test results show that the Decision Tree model achieves an accuracy of 85% with a macro-AUC value of 0.448. These results prove that the Decision Tree algorithm can be used for patient condition classification with reasonably good performance, although the model still tends to make prediction errors in some minority classes.
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Silaban et al. (2026) studied Heart disease (n=131). Decision Tree algorithm on an IoT platform was evaluated on Model classification accuracy. An IoT-based Decision Tree algorithm achieved an accuracy of 85% and a macro-AUC of 0.448 for classifying patient conditions based on real-time vital parameters.
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