Early Detection of Diabetic Mellitus Based On Modeling Techniques
Pydipala Laxmikanth1, Bhramaramba Ravi2
1P.Laxmikanth, Ph.D from GIT, Gandhi Institute of Technology and Management, University in Visakhapatnam, (A P) India.
2Dr. Bhramaramba Ravi, Ph.D from GIT, Gandhi Institute of Technology and Management, University in Visakhapatnam, (A P) India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1778-1783 | Volume-9 Issue-1, October 2019 | Retrieval Number: F9060088619/2019©BEIESP | DOI: 10.35940/ijeat.F9060.109119
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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: Rapid growth of population in particular in elderly, signifies the issues of healthcare have become a concern. Also the lifestyle changes together with social and economical factors influences the cause of disease generation among the generated diseases, diabetic disease is mostly populated. Therefore effective measures are to be taken so that the early wakeup with regard to disease treatment helps to minimize the after effects. In this article, a novel methodology based on Gaussian mixture model built for analyzing the patients and help to identify the disease during the primitive stage. The methodology is presented based on PIMA INDIAN DATASET. The Results derived showcase efficacy of the developed method. Index Terms: Diabetes mellitus, Gaussian mixture model, disease detection, probabilistic method, early identification
Keywords: Carbon Nano Tube FET (CNTFET), Content Addressable Memory (CAM) Cell, SRAM, MOSFET.