doi: 10.7763/IJET.2011.V3.271
Predicting Deep Venous Thrombosis Using Binary Decision Trees
- 1 Pace University, New York, USA
- 2 Montefiore Medical Center, New York, USA
- 3 Department of Computer Science, Fairleigh Dickinson University, New Jersey, USA
Abstract
An intrinsic disease where blood clots form in a deep vein in the body is known as Deep Venous Thrombosis (DVT). 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.
Keywords
- Decision Trees
- DVT and Genetic Algorithm
How to Cite
Christopher Nwosisi, Sung-Hyuk Cha, Charles C. Tappert, Yoo Jung An, and Evan Lipsitz, "Predicting Deep Venous Thrombosis Using Binary Decision Trees," International Journal of Engineering and Technology, vol. 3, no. 5, pp. 467-472, 2011. https://doi.org/ 10.7763/IJET.2011.V3.271
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).