Diagnosing Diabetes Using Support Vector Machine in Classification Techniques

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

Title:
Diagnosing Diabetes Using Support Vector Machine in Classification Techniques

Authors:
T. Padma Nivethitha | A. Raynuka | Dr. J. G. R. Sathiaseelan
DOI:
https://doi.org/10.31142/ijtsrd18251

Cite This Article :

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, pp.2208-2214, URL: https://www.ijtsrd.com/papers/ijtsrd18251.pdf

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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.

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

Publication Details:

Unique Identification Number : IJTSRD18251

Published In : Volume-2 | Issue-5, August 2018

Page Number(s) : 2208-2214

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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