TITLE

Type 2 Diabetes Prediction Using Multinomial Logistic Regression

AUTHOR(S)
Priya, R. Janani; Umamaheswari, K.
PUB. DATE
July 2014
SOURCE
Australian Journal of Basic & Applied Sciences;Jul2014, Vol. 8 Issue 10, p31
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Diabetes mellitus is a one of the most important chronic disease and has become a major public health challenge in the recent world. Currently data-mining approaches have been used to analyze and predict the probability of people getting affected by diabetes. Diabetes can be effectively diagnosed using the proposed data mining model techniques. The Bayes network outperforms other classification methods for type-2 diabetes detection. Moving a step ahead to improve the diagnostic efficiency, this paper proposes the use of Multinomial Logistic Regression for detecting the type-2 Diabetes. This new approach proves higher effectiveness when compared to Bayes network. Using Pima Indian Dataset experiments were performed and the result shows that the Multinomial Logistic Regression outperforms the Bayes network classification method.
ACCESSION #
97368458

 

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