The Research on the Boosting Decision Tree Algorithm for Intelligent Medical System

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Intelligent Medical Systems currently has been used in most advanced hospitals worldwide, and most of theses systems have the function module of guiding-patient-diagnosis, which makes the patient use this feature to get a preliminary treatment opinion based on their patients condition. In this paper, the way of enhancing the effect of guiding-patient-diagnosis with boosting decision tree algorithm is researched, and the paper describes the specific applying process of the algorithm. Finally, boosting decision tree algorithm prove to improve the efficiency of intelligent guiding function, making intelligent guiding diagnosis function accurate and reliable, which save the health care resources and the time of doctors and patients.

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365-368

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July 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Charqane, K.; Pasin, M.; Dominici, F.; Fausto, G.: Medical intelligent system for assisted living — Proof of concept, Satellite Telecommunications (ESTEL), 2012 IEEE First AESS European Conference, (2012), pp.1-6.

DOI: 10.1109/estel.2012.6400140

Google Scholar

[2] Sagi, A.; Sabo, A.; Kuljic, B.; Szakall, T.: Intelligent system and human factor caused medicalerrors, Intelligent system and human factor caused medicalerrors, (2013), pp.370-380.

DOI: 10.1109/sisy.2013.6662579

Google Scholar

[3] Kargupta, H.; Byung-Hoon Park; Dutta, H.: Orthogonal decision trees. Knowledge and Data Engineering, IEEE Transactions, Vol. 18, No. 8, (2006), pp.1028-1042.

DOI: 10.1109/tkde.2006.127

Google Scholar

[4] Grossmann, E.: AdaTree: Boosting a Weak Classifier into a DecisionTree, Computer Vision and Pattern Recognition Workshop, 2004. CVPRW '04. Conference, (2004), p.105.

DOI: 10.1109/cvpr.2004.296

Google Scholar

[5] Miao He; Junshan Zhang; Vittal.: A data mining framework for online dynamic security assessment: Decision trees, boosting, and complexity analysis, Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES , (2012), pp.1-8.

DOI: 10.1109/isgt.2012.6175766

Google Scholar

[6] Huimin Zhao, et al.: Constrained cascade generalization of decision trees, Knowledge and Data Engineering, IEEE Transactions, Vol. 16, No. 8, (2004), pp.727-739.

DOI: 10.1109/tkde.2004.3

Google Scholar