Detection of Diabetes using Biosensors
B.Venkateswarulu1, Nandita.Y2, M.Hitesh3, M.Vamsi Krishna4
1B.VENKATESWARULU, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2NANDITA.Y, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
3M.HITESH, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
4M.VAMSI KRISHNA, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8705-8708 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8923118419/2019©BEIESP | DOI: 10.35940/ijrte.D8923.118419
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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 is the widespread disease in the world. During the last few decades there is a huge increase in the number of diabetic patients. Biosensor is a device which is used to detect diabetes. It converts biological reading into electrical pursuing that helps the patients to predict their diabetic levels. It consists of a biological recognition element and a chemical sensor. They are high-technology tracking tools that measures sugar levels in a quick, highly sensible fashion. We mainly focus on the glucose-oxidase biosensor which uses machine learning techniques and regression methods. Detection of diabetes can be done in many forms like with a radiofrequency biosensor chip, optical fiber biosensor, micro-fluidic biosensor. Optical biosensor is placed in contact lens to detect glucose levels within the tears and give the accurate prediction. There were some early versions of glucose-sensing devices. There are different existing techniques for the detection. But the preferred method is using the continuous glucose observing system. This method offers a good control of diabetes by using real time data. However, there are few provokes identified with the accomplishment of precise and dependable glucose observing. Further consistency and improvements in the development of biosensors to meet their goals are required.
Keywords: Diabetes Mellitus, Physiological Parameters, Support Vector Machines, Continues Glucose Monitoring, Classification and Regression.
Scope of the Article: Classification.