Miniature Medical Car Didukung Dengan Pulse Arm Band Berbasis Algoritma XGBoost Dalam Deteksi Pola Abnormal Detak Jantung
DOI:
https://doi.org/10.51903/p4ey0516Keywords:
IoT, Heart Rate Monitoring, XGBoost, Wearable Device, Anomaly DetectionAbstract
The increasing prevalence of cardiovascular disorders highlights the need for heart rate monitoring systems that are not only accurate but also capable of providing rapid responses to abnormal conditions. This study aims to develop an Internet of Things (IoT)-based heart rate monitoring system integrated with the Extreme Gradient Boosting (XGBoost) algorithm to detect abnormal heart rate patterns in real time and trigger an automated response through a Miniature Medical Car. A quantitative simulation-based research method was employed, in which heart rate signals were collected using an ESP32-C3 Mini–based Pulse Armband equipped with a MAX30102 sensor and transmitted to a cloud server for processing through a data-mining pipeline. The dataset consisted of 800 samples categorized into normal and abnormal classes, which were transformed into 14 statistical and variability-based features for model training. The results indicate that the XGBoost model achieved 94% accuracy, 93.3% precision, 93.3% recall, an F1-score of 0.933, and an ROC-AUC of 0.96, demonstrating strong performance in distinguishing normal from abnormal heart rate patterns. Integration with the miniature car enables the system to deliver an automated physical response when abnormal patterns are detected. The study contributes an efficient real-time IoT–ML architecture, compact statistical feature engineering for physiological signals, and a robotic response mechanism. These findings imply potential applications in remote medical monitoring, public-area emergency alert systems, and low-cost assistive devices.
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