Indonesian Journal of Electrical Engineering and Computer Science
Vol 27, No 1: July 2022

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

Rahnuma Mahzabin (University of Liberal Arts Bangladesh (ULAB))
Fahim Hossain Sifat (University of Liberal Arts Bangladesh (ULAB))
Sadia Anjum (University of Liberal Arts Bangladesh (ULAB))
Al-Akhir Nayan (Chulalongkorn University)
Muhammad Golam Kibria (University of Liberal Arts Bangladesh (ULAB))



Article Info

Publish Date
01 Jul 2022

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’s 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 victim’s body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed model's performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The system’s performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.

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