A Novel Intelligent Medical Decision Support Model Based on Soft Computing and IoT

Mohamed Abdel-Basset, Gunasekaran Manogaran, Abduallah Gamal, Victor Chang

Research output: Contribution to journalArticlepeer-review

152 Citations (Scopus)


Internet of Things (IoT) has gain the importance with the growing applications in the fields of ubiquitous and context-aware computing. In IoT, anything can be a portion of it, whether it is unintelligent objects or sensor nodes; thus extremely different kinds of services can be developed. In this regard, data storage, resource management, service creation and discovery, and resource and power management would facilitate advanced mechanism and much better infrastructure. Cloud computing and fog computing play an important role when the quantity of data and information IoT are critical. Thus, it would not be potential for standalone strength forced IoT to handle. Cloud of things is an integration of IoT with cloud computing or fog computing which can aid to realize the objectives of evolving IoT and future Internet. Fog computing is an expansion to the notion of cloud computing to the network brim, making it suitable for IoT and other implementations that need real-time and fundamental interactions. Regardless of many virtually and services unlimited resources presented by cloud-like intelligent building monitoring and others, it yet countenances various difficulties when interfering many smart things in human's life. Mobility, response time, and location consciousness are the most prominent problems. Fog and mobile edge computing have been established, to get rid of these difficulties of cloud computing. In this article, we suggest a novel framework based on computer propped diagnosis and IoT to detect and observe type-2 diabetes patients. The recommended healthcare system aims to obtain a better accuracy of diagnosis with mysterious data. The overall experimental results indicate the validity and robustness of our proposed algorithms.

Original languageEnglish
Article number8787865
Pages (from-to)4160-4170
Number of pages11
JournalIEEE Internet of Things Journal
Issue number5
Publication statusPublished - May 2020
Externally publishedYes


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