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Analyze Data Mining Algorithms For Prediction Of Diabetes
Priya B. Patel,  Parth P. Shah,  Himanshu D. Patel
Purpose of data mining is to extract useful information from large collection of data. Before understanding what is Diabetes we need to understand role of insulin in our body. Insulin serve “Gateway” to open body cells, it allows our body to use the glucose for energy. Insulin controls glucose level in our body. Diabetes is a disease in which level of glucose in blood is increase. Traditionally diabetes diagnosed by physical and comical test, But it not give accurate result.
To overcome this limitation we make prediction of disease using different Data Mining algorithm for prediction and diagnosis of diabetes mellitus. The main data mining algorithms discussed in this paper are Gaussian Naive Bayes, KNN, SVM and Decision Tree. The data set chosen for experimental simulation is based on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of Machine Learning databases.
Keywords- Data mining, Diabetes, GNB algorithm, KNN algorithm, SVM algorithm, Decision tree algorithm.
Unique Identification Number - IJEDR1703069Page Number(s) - 466-473Pubished in - Volume 5 | Issue 3 | July 2017DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Priya B. Patel,  Parth P. Shah,  Himanshu D. Patel,   "Analyze Data Mining Algorithms For Prediction Of Diabetes"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 3, pp.466-473, July 2017, Available at :http://www.ijedr.org/papers/IJEDR1703069.pdf