Predictive Maintenance of IoT-Enabled Biomedical Sensors Using Machine Learning to Enhance Patient Safety

Internet of Things (IoT)-enabled biomedical sensors support continuous patient monitoring but may experience sensor drift, battery deterioration, communication interruptions, and hardware faults. Such failures can compromise measurement availability and reliability. This paper proposes a machine-learning-based predictive maintenance framework combining device-health telemetry, communication statistics, and verified maintenance records. The architecture comprises data acquisition, secure gateway communication, preprocessing, fault prediction, and maintenance alert management. Candidate methods include Random Forest, Isolation Forest, gradient boosting, and temporal models, compared against a transparent rule-based baseline. An evaluation protocol is defined using precision, recall, precision–recall area under the curve, false alarms per device-day, warning lead time, and inference latency. The work also addresses model drift, cybersecurity, and the separation of maintenance alerts from clinical decision-making. As a framework paper, it reports no measured model performance or patient outcomes. Experimental validation is necessary before operational or clinical claims can be made.