论文标题

区块链相关的机器学习和基于IoT的低血糖检测系统具有自动注射功能

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

论文作者

Mahzabin, Rahnuma, Sifat, Fahim Hossain, Anjum, Sadia, Nayan, Al-Akhir, Kibria, Muhammad Golam

论文摘要

低血糖是由低血糖引起的一种不愉快的现象。该疾病会导致一个人死亡或高水平的身体损害。为了避免严重损害,患者需要糖。该研究旨在实施自动系统来检测低血糖并进行自动注射以挽救生命。接收物联网(IoT)的好处,传感器数据是使用超文本传输​​协议(HTTP)协议传输的。为了确保与健康相关数据的安全性,使用区块链技术。葡萄糖传感器和智能手表数据通过雾处理并发送到云。提出了一种随机的森林算法并利用来决定降血糖事件。当检测到降血糖事件时,该系统向移动应用程序和自动注入装置发送了通知,将凝结的糖推入受害者体内。 XGBOOST,K-Nearest邻居(KNN),支持向量机(SVM)和决策树已实施,以比较提出的模型性能。随机森林的测试准确性比检测降血糖事件的其他模型要好。在多种条件下测量了系统性能,并实现了令人满意的结果。该系统可以使低血糖患者受益于这种疾病。

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.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源