Diagnosing Diabetes Using Support Vector Machine in Classification Techniques


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Article type :

Original Article

Author :

T. Padma Nivethitha | A. Raynuka | Dr. J. G. R. Sathiaseelan

Volume :

2

Issue :

5

Abstract :

Data mining is an iterative development inside which development is characterized by exposure, through either usual or manual strategies. In this paper, we proposed a model to ensure the issues in existing framework in applying data mining procedures specifically Classification and Clustering which are connected to analyze the type of diabetes and its significance level for each patient from the data gathered. It includes the illnesses plasma glucose at any rate held value. The research describes algorithmic discussion of Support vector machine SVM , Multilayer perceptron MLP , Rule based classification algorithm JRIP , J48 algorithm and Random Forest. The result SVM algorithm best. The best outcomes are accomplished by utilizing Weka tools. T. Padma Nivethitha | A. Raynuka | Dr. J. G. R. Sathiaseelan "Diagnosing Diabetes Using Support Vector Machine in Classification Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18251.pdf Paper URL: http://www.ijtsrd.com/computer-science/data-miining/18251/diagnosing-diabetes-using-support-vector-machine-in-classification-techniques/t-padma-nivethitha

Keyword :

Data Mining, Diabetes, Classification, Clustering, SVM, MLP, JRIP, J48 and Random Forest, Weka.
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