Existential Methods on Diabetes Detection using Machine Learning
Vaishali Yogesh Baviskar

Mrs. Vaishali Y. Baviskar, Assistant Professor, G. H. Raisoni Institite of Enginerring and Technology, Wagholi, Pune, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3034-3039 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7157038620/2020©BEIESP | DOI: 10.35940/ijrte.F7157.038620

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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: Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.
Keywords: SVM, NN, Naïve Bayes, KNN, Diabetes
Scope of the Article: Machine Learning.