Constructing binary decision trees for predicting Deep Venous Thrombosis

Christopher Nwosisi; Sung Hyuk Cha; Yoo Jung An; Charles C. Tappert; Evan Lipsitz

doi:10.1109/ICSTE.2010.5608901

Constructing binary decision trees for predicting Deep Venous Thrombosis

Christopher Nwosisi, Sung Hyuk Cha, Yoo Jung An, Charles C. Tappert, Evan Lipsitz

Cardiovascular & Thoracic Surgery

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Deep Venous Thrombosis (DVT) is an intrinsic disease where blood clots form in a deep vein in the body. Since DVT has a high mortality rate, predicting it early is important. Decision trees are simple and practical prediction models but often suffer from excessive complexity and can even be incomprehensible. Here a genetic algorithm is used to construct decision trees of increased accuracy and efficiency compared to those constructed by the conventional ID3 or C4.5 decision tree building algorithms. Experimental results on two DVT datasets are presented and discussed.

Original language	English (US)
Title of host publication	ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings
Pages	V1121-V1124
DOIs	https://doi.org/10.1109/ICSTE.2010.5608901
State	Published - 2010
Event	2010 2nd International Conference on Software Technology and Engineering, ICSTE 2010 - San Juan, PR, United States Duration: Oct 3 2010 → Oct 5 2010

Publication series

Name	ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings
Volume	1

Other

Other	2010 2nd International Conference on Software Technology and Engineering, ICSTE 2010
Country/Territory	United States
City	San Juan, PR
Period	10/3/10 → 10/5/10

Keywords

Decision tree
Deep venous thrombosis
Genetic algorithm

ASJC Scopus subject areas

Software

Access to Document

10.1109/ICSTE.2010.5608901

Cite this

Nwosisi, C., Cha, S. H., An, Y. J., Tappert, C. C., & Lipsitz, E. (2010). Constructing binary decision trees for predicting Deep Venous Thrombosis. In ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings (pp. V1121-V1124). Article 5608901 (ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings; Vol. 1). https://doi.org/10.1109/ICSTE.2010.5608901

Constructing binary decision trees for predicting Deep Venous Thrombosis. / Nwosisi, Christopher; Cha, Sung Hyuk; An, Yoo Jung et al.
ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings. 2010. p. V1121-V1124 5608901 (ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nwosisi, C, Cha, SH, An, YJ, Tappert, CC & Lipsitz, E 2010, Constructing binary decision trees for predicting Deep Venous Thrombosis. in ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings., 5608901, ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings, vol. 1, pp. V1121-V1124, 2010 2nd International Conference on Software Technology and Engineering, ICSTE 2010, San Juan, PR, United States, 10/3/10. https://doi.org/10.1109/ICSTE.2010.5608901

Nwosisi C, Cha SH, An YJ, Tappert CC, Lipsitz E. Constructing binary decision trees for predicting Deep Venous Thrombosis. In ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings. 2010. p. V1121-V1124. 5608901. (ICSTE 2010 - 2010 2nd International Conference on Software Technology and Engineering, Proceedings). doi: 10.1109/ICSTE.2010.5608901

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abstract = "Deep Venous Thrombosis (DVT) is an intrinsic disease where blood clots form in a deep vein in the body. Since DVT has a high mortality rate, predicting it early is important. Decision trees are simple and practical prediction models but often suffer from excessive complexity and can even be incomprehensible. Here a genetic algorithm is used to construct decision trees of increased accuracy and efficiency compared to those constructed by the conventional ID3 or C4.5 decision tree building algorithms. Experimental results on two DVT datasets are presented and discussed.",

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N2 - Deep Venous Thrombosis (DVT) is an intrinsic disease where blood clots form in a deep vein in the body. Since DVT has a high mortality rate, predicting it early is important. Decision trees are simple and practical prediction models but often suffer from excessive complexity and can even be incomprehensible. Here a genetic algorithm is used to construct decision trees of increased accuracy and efficiency compared to those constructed by the conventional ID3 or C4.5 decision tree building algorithms. Experimental results on two DVT datasets are presented and discussed.

AB - Deep Venous Thrombosis (DVT) is an intrinsic disease where blood clots form in a deep vein in the body. Since DVT has a high mortality rate, predicting it early is important. Decision trees are simple and practical prediction models but often suffer from excessive complexity and can even be incomprehensible. Here a genetic algorithm is used to construct decision trees of increased accuracy and efficiency compared to those constructed by the conventional ID3 or C4.5 decision tree building algorithms. Experimental results on two DVT datasets are presented and discussed.

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Constructing binary decision trees for predicting Deep Venous Thrombosis

Abstract

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Keywords

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