Risk Assessment of Diabetic Retinopathy using Data Mining Techniques
Manoj Vairalkar1, Vijaya Kamble2, Kalpana Malpe3
1Mr. Manoj Vairalkar, Department of Computer Science and Technology Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India.
2Ms. Vijaya Kamble, Department of Computer Science and Technology Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India.
3Ms. Kalpana Malpe, Department of Computer Science and Technology Guru Nanak Institute of Engineering & Technology, Nagpur, Maharashtra, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 24 May 2019 | PP: 168-171 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F22280486S219/19©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: One of the serious issue diabetic patients experiences is Diabetic Retinopathy and visual impairment. Since the quantity of diabetes patients is ceaselessly expanding, these outcomes in an increment in the information too. In wellbeing observing diabetes is the regular wellbeing issue these days, which influences people groups. There are different information mining strategies and calculation is utilized for finding the diabetes. Neural Network, Artificial neural fluffy impedance framework, K Nearest-Neighbor (KNN), Genetic Algorithm, Back Propagation calculation and so forth. These systems and the calculations give the better result to the general population and the specialists with respect to the conclusion of the diabetes. There are numerous systems and calculations that assistance to analyze DR in retinal fundus pictures. This paper audits characterizes and thinks about the calculations and procedures recently proposed so as to grow better and progressively compelling calculations.
Keywords: Data Mining, Artificial neural fuzzy Interference System, K-Nearest-Neighbor (KNN), Machine Learning (ML), Principal Component Analysis (PCA).
Scope of the Article: Machine Learning