Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/1844
Title: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS APPLIED TO PREDICTIVE DIABETES DATA
Authors: K, Sathiyakumari
V, Pream Sudha
Keywords: Machine Learning
Diabetes Mellitus
Classification
Naive Bayes
Multi Layer Perceptrons
Logistic Model Trees
Nearest Neighbour with Generalized Exemplars
WEKA
Issue Date: 25-Nov-2009
Publisher: CiiT International Journal of Data Mining and Knowledge Engineering
Abstract: Healthcare industry encompasses abundant data, which is increasing everyday. Conversely, tools for analyzing these records are incredibly less. Machine learning provides a lot of techniques for solving diagnostic problems in a variety of medical domains. Intelligent systems are able to learn from machine learning methods, when they are provided with a set of clinical cases as training set. This paper aims at a comparative study of widely used supervised classification algorithms – Naïve Bayes, Multi Layer Perceptrons, Logistic Model Trees, and Nearest Neighbor with Generalized Exemplars applied to predictive diabetes dataset. The machine learning algorithms used in this study are chosen for their representability and diversity. They are evaluated on the basis of their accuracy, learning time and error rates.
URI: http://ciitresearch.org/dl/index.php/dmke/article/view/DMKE112009005
http://localhost:8080/xmlui/handle/123456789/1844
ISSN: 0974 – 9578
Appears in Collections:International Journals

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