Design of an Adaptive Machine Learning Model for Early Prediction of Critical Patient Deterioration in IoT-Enabled Healthcare Systems

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@article{1715135,
      author = {Nwobodo-Nzeribe, Chikezie C. D., Ndulue T., Obonyano K. N., Utibe V.; Asoronye G. O.},
      title = {Design of an Adaptive Machine Learning Model for Early Prediction of Critical Patient Deterioration in IoT-Enabled Healthcare Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2807-2816},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715135.pdf},
      abstract = {The dynamic development of the IoT-based technologies in the healthcare sector has allowed to monitor the physiological indicators of the patients continuously, but the methods of the early warning that are used traditionally based on the threshold values frequently cannot provide an opportunity to notice the critical worsening in time. This paper introduces a flexible hybrid deep learning network consisting of Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D-CNN), and Gated Recurrent Unit (GRU) networks as a predictor of early patient deterioration in the Internet of Things (IoT)-based healthcare systems. IoT-monitored physiological time-series data (heart rate, blood pressure, respiratory rate, temperature, and SpO 2 ) were gathered using both IoT sensors and retrospective clinical data. Preprocessing of the data included filtering of noise, normalisation, filling in of missing values and breaking down into fixed-length sequences. The hybrid model combines the static, short-term and long-term time data to make real-time predictions to overcome the shortcomings of traditional early-warning models. Experimental results demonstrate that the hybrid model outperforms individual sub-models, achieving 96.8% accuracy, 95.7% precision, 97.2% recall, 96.4% F1-score, and 0.983 ROC-AUC, with significantly reduced false positives and false negatives. The results suggest that the suggested framework will be able to issue reliable and timely alerts about vital patient deterioration, contribute to proactive clinical interventions, and enhance patient safety in IoT healthcare settings.},
      keywords = {Adaptive Machine Learning; Hybrid Deep Learning; IoT-Enabled Healthcare; Early Prediction; Patient Deterioration},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715135}
  }