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Abstract

Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and categorize emergency cases for precise action.Current systems use manual examination resulting in delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of Things (IoT) based model, created using Balena fin with Raspberry pi compute module.It is used for determining emergency cases, in pediatric section, specifically the triage section.It is later tested using hospital data that represents the vital parameters in pediatric.Our approach entails designing and setting up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it to the cloud server database via Message Queuing Telemetry Transport (MQTT).Later, we perform machine learning on the data by training a model and finally develop a Plotly Dash analytical application integrating the model for visualization near real-time.Findings show that emergency cases are detected using vital body parameters which include the body temperature, oxygen levels, heart rate and the age.The model indicates a 97% accuracy.In conclusion, children's emergency cases are detected in time using IoT gadgets and machine learning classification.