Anomaly Detection of Patient Data in Public Hospital used Internet of Things Sensors

Increasing number of patients doing treatment in a public hospital become as issue for the management. Limited number of equipment to detect abnormal patient and support from medical staff. This research aims to detect on the anomaly detection of hospital patient data through the utilization of Internet of Things (IoT) sensors. With the increasing integration of IoT devices in healthcare settings, such as sensors monitoring vital signs like temperature, blood pressure, and heart rate, the need for robust anomaly detection mechanisms becomes imperative. The research explores advanced algorithms and machine learning techniques to identify irregular patterns or outliers in patient data, aiming to enhance the early detection of potential health issues or abnormalities. Leveraging the vast amount of data generated by IoT sensors in hospital environments, the research aims to contribute to the development of more efficient and accurate anomaly detection systems, ultimately improving the quality of patient care and facilitating timely intervention by healthcare professionals. The findings of this research have significant implications for the evolving landscape of healthcare technologies, emphasizing the importance of ensuring data integrity and patient safety in the era of IoT-driven healthcare solutions. Patient data shows in graph to see how the anomaly indication for the patient. Multi sensor with parallel system will develop in future research plan.