文章基本信息

标题：Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning
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作者：Abdullah Suhail Abed ; Brwa Khalil Abdullah Ahmed ; Sura Khalil Ibrahim 等
期刊名称：Webology
印刷版ISSN：1735-188X
出版年度：2022
卷号：19
期号：1
页码：3410-3418
DOI：10.14704/WEB/V19I1/WEB19224
语种：English
出版社：University of Tehran
摘要：Scholars and medical professionals have recognizes the importance of electronic medical monitoring services for tracking elderly people's health. These platforms generate a large amount of data, requiring privacy and data security. on the contrary, Using Software Defined Networking (SDN) to maintain network efficiency and flexibility, which is especially important in the case of healthcare observation, could be a viable solution. Moreover, machine learning can additionally utilized as a game changing tool which incorporated with SDN for optimal level of privacy and security. Even so, integrating SDN into machine learning, which heavily relies on health sensors of patients, is incredibly difficult. In this paper, an Integrate Medical Platform (IMP) with a focus on SDN and Machine learning integration is proposed. We produce a platform that reduces complexity by identifying high level SDN regulations based on the extracted flow classes and utilizing machine learning traffic flow classification techniques. F or various types of traffic, We employ supervised learning approaches based on models that have already been trained. We use four algorithms for supervised learning: Random forest, Logistic Regression classifiers, K-NN, and SVM, with different characteristics. Finally, we evaluated IMP by using accuracy, precision, TPR, TNR, FPR, MAE, and energy consumption.
关键词：SDN;e-Health;Traffic Classification;Supervised Machine Learning