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Cascaded Modeling for PIMA Indian Diabetes Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 11
Year of Publication: 2016
Authors:
M.S. Barale, D.T. Shirke
10.5120/ijca2016909426

M S Barale and D T Shirke. Article: Cascaded Modeling for PIMA Indian Diabetes Data. International Journal of Computer Applications 139(11):1-4, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {M.S. Barale and D.T. Shirke},
	title = {Article: Cascaded Modeling for PIMA Indian Diabetes Data},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {11},
	pages = {1-4},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

This paper develops the cascaded models for classification of PIMA Indian diabetes database. The k-nearest neighbour method is used to impute the missing data and the processed data is used for further classification. This is done in two steps, in first step k-means clustering algorithm is used for extracting hidden patterns in data set then in second step the classification is done by using suitable classifier. k-means algorithm combined with artificial neural network classifier and k-means algorithm combined with logistic regression classifier achieve classification accuracy above 98%.

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Keywords

Missing data, Clustering, Classification