Author(s): V. Mareeswari, Saranya R, Mahalakshmi R, Preethi E

Email(s): vmareeswari@vit.ac.in

DOI: 10.5958/0974-360X.2017.00199.8   

Address: V. Mareeswari* , Saranya R, Mahalakshmi R, Preethi E
Department of Software Engineering, School of Information Technology and Engineering, VIT University, Vellore.
*Corresponding Author

Published In:   Volume - 10,      Issue - 4,     Year - 2017


ABSTRACT:
Diabetes mellitus is one of the world’s major diseases. Millions of people are affected by the disease. The risk of diabetes is increasing day by day and is found mostly in women than men. The diagnosis of diabetes is a tedious process. So with improvement in science and technology it is made easy to predict the disease. The purpose is to diagnose whether the person is affected by diabetes or not using K Nearest Neighbor classification technique. The diabetes dataset is a taken as the training data and the details of the patient are taken as testing data. The training data are classified by using the KNN classifier and secondly the target data is predicted. KNN algorithm used here would be more efficient for both classification and prediction. The results are analyzed with different values for the parameter k.


Cite this article:
V. Mareeswari, Saranya R, Mahalakshmi R, Preethi E. Prediction of Diabetes Using Data Mining Techniques. Research J. Pharm. and Tech. 2017; 10(4): 1098-1104. doi: 10.5958/0974-360X.2017.00199.8

Cite(Electronic):
V. Mareeswari, Saranya R, Mahalakshmi R, Preethi E. Prediction of Diabetes Using Data Mining Techniques. Research J. Pharm. and Tech. 2017; 10(4): 1098-1104. doi: 10.5958/0974-360X.2017.00199.8   Available on: https://rjptonline.org/AbstractView.aspx?PID=2017-10-4-26


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DOI: 10.5958/0974-360X 

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