Risk Analysis of Diabetes using IoT and Deep Learning
Sujaritha.M1, Monica Murugesan2, Bhuvana MK3, Saleekha4
1Sujaritha. M, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (TamilNadu), India.
2Monica Murugesan, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (TamilNadu), India.
3Bhuvana MK, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (TamilNadu), India.
4Saleekha, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore (TamilNadu), India.
Manuscript received on 04 May 2019 | Revised Manuscript received on 09 May 2019 | Manuscript Published on 13 May 2019 | PP: 222-227 | Volume-8 Issue-7S May 2019 | Retrieval Number: G10430587S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetes mellitus is a most common disease faced by most of patients can have uncontrollable glucose level can lead to chronic disease to prevent this risk of higher chances of chronic diseases which can be implemented using Internet of Things(IoT) which is applied in various areas for solving problems of healthcare involved in monitoring and diagnosis of different parts of body using wearables or biosensors. In the proposed system, IoT devices and cloud technologies are connected to transfer data and execute the decisions on well-defined rules and deep learning technique is applied on diabetes data to decide the risk of diabetic patient which is solved by defining rules, system can understand the which data lies under which partition and knowledge representation can be made using the result the system can decide whether to suggest lifestyle modifications or proper in-take medication for improving their health and reduce adverse reactions in other parts of body or preventing to cause psychological effects.
Keywords: Diabetes Mellitus, Deep Learning, Internet of Things, Lifestyle Modifications.
Scope of the Article: Computer Science and Its Applications