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<p>To address the limitations of single-function medical products, which often lack synchronized monitoring, storage, and analysis capabilities, this study proposes a comprehensive computerized nursing model for multi-parameter vital signs monitoring based on the Model-View-Controller (MVC) architectural pattern. The integrated system combines wearable hardware for data acquisition, a mobile application for real-time display and user interaction, and a cloud-based backend for advanced signal processing and machine learning. By applying the synthetic minority over-sampling technique (SMOTE) to balance the training data, the system’s StackingClassifier achieves 96% accuracy for arrhythmia detection on the MIT-BIH dataset. Furthermore, the model’s robustness was validated on a large-scale, non-clinical dataset from 250 participants, achieving an excellent Area Under the Curve (AUC) of 0.974 in real-world arrhythmia detection. This experimental validation also confirms high precision in measuring heart rate, body temperature, and step count when compared to commercial devices. This work demonstrates a scalable and maintainable solution for reliable self-health management and cardiovascular disease prevention.</p>

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Construction of computer nursing model for ECG monitoring based on MVC development pattern

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月5日

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  • Junxia Liu,
  • Yan Niu,
  • Guanghui Zhang,
  • Yanhong Zhang,
  • Jiaosheng Lü

摘要

To address the limitations of single-function medical products, which often lack synchronized monitoring, storage, and analysis capabilities, this study proposes a comprehensive computerized nursing model for multi-parameter vital signs monitoring based on the Model-View-Controller (MVC) architectural pattern. The integrated system combines wearable hardware for data acquisition, a mobile application for real-time display and user interaction, and a cloud-based backend for advanced signal processing and machine learning. By applying the synthetic minority over-sampling technique (SMOTE) to balance the training data, the system’s StackingClassifier achieves 96% accuracy for arrhythmia detection on the MIT-BIH dataset. Furthermore, the model’s robustness was validated on a large-scale, non-clinical dataset from 250 participants, achieving an excellent Area Under the Curve (AUC) of 0.974 in real-world arrhythmia detection. This experimental validation also confirms high precision in measuring heart rate, body temperature, and step count when compared to commercial devices. This work demonstrates a scalable and maintainable solution for reliable self-health management and cardiovascular disease prevention.