Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

TL;DR

This paper presents an integrated IoT and blockchain-based system for automatic hypoglycemia detection and treatment, utilizing machine learning algorithms to improve patient safety and response times.

Contribution

It introduces a novel system combining IoT, blockchain, and machine learning for automatic hypoglycemia detection and treatment with auto-injection capabilities.

Findings

Random Forest achieved 94.2% accuracy in hypoglycemia detection.

The system successfully integrated IoT sensors, blockchain, and machine learning for real-time response.

Auto-injection feature effectively delivers sugar during hypoglycemic events.

Abstract

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victims body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed models performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The systems performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.