Paper Info
Title
A statistical decision tree algorithm for medical data stream mining
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.
Year
DOI
Venue
2013
10.1109/CBMS.2013.6627823
Computer-Based Medical Systems
Keywords
Field
DocType
data mining,decision trees,diseases,medical administrative data processing,pattern classification,statistical analysis,StARMiner Tree,computational resources,incremental decision tree classifier,incremental techniques,medical data mining,medical data stream mining,medical data stream processing,medical datasets,medical disease diagnosis,medical environment,reading sample number,statistical decision tree algorithm,very fast decision tree technique
Decision tree,Data mining,Data stream mining,Data stream,Computer science,Execution time,Artificial intelligence,ID3 algorithm,Classifier (linguistics),Decision tree learning,Machine learning,Incremental decision tree
Conference
Citations 
PageRank 
References 
3
0.40
2
Authors
2
Name
Order
Citations
PageRank
Mirela Teixeira Cazzolato140.79
M. X. Ribeiro26812.72