NDLI: A statistical decision tree algorithm for medical data stream mining

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2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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A statistical decision tree algorithm for medical data stream mining

Content Provider	IEEE Xplore Digital Library
Author	Cazzolato, M.T. Ribeiro, M.X.
Copyright Year	2013
Abstract	The use of computational resources can improve the diagnosis of medical diseases as a second opinion. Due to the large amount of data obtained daily, incremental techniques have been proposed to process medical data stream. In this paper we present an incremental decision tree classifier called StARMiner Tree (ST), which is based on Very Fast Decision Tree (VFDT) technique, to mine medical data. Different from VFDT, our proposed method ST does not depend on the number of reading samples to split a node. Because of it, ST is less conservative and describes the data since their first samples, being appropriate to be employed in medical environment, where not always a large number of data samples are available. We applied ST to four medical datasets, comparing the ST performance to the VFDT. The results indicated that ST is well-suited to deal with medical data streams, presenting high accuracy and low execution time.
Starting Page	389
Ending Page	392
File Size	343385
Page Count	4
File Format	PDF
ISBN	9781479910533
DOI	10.1109/CBMS.2013.6627823
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-20
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Accuracy Training Skin Data models Association rules
Content Type	Text
Resource Type	Article

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