Decision Tree Model in the Diagnosis of Breast Cancer
Published in 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), 2017
Abstart
Breast cancer is the second leading cause of cancer death in women. At the same time, it is one of the most curable cancer if it could be diagnosed early. More and more researchers have confirmed that the decision tree model has a good ability to accurately diagnose. This paper presents a diagnostic method for breast cancer based on the decision tree model combined with feature selection. Experiments were conducted on different training test divisions of the Wisconsin Breast Cancer Data Set (WBCD), a common method used by researchers to diagnose breast cancer based on machine learning methods. In this paper, in order to reduce the complexity of the decision tree model, this paper proposed to delete some highly relevant features of … After data correlation and independence tests, it finally chosed the tumor thickness, cell shape consistency, single epithelial cell size and mitosis as a subset of the decision tree model. Experimental results show that the classification accuracy (94.3%) significantly outperforms the state-of-theart method with respect to a variety of metrics.
Citations
@INPROCEEDINGS{8789297,
author={L. {Yi} and W. {Yi}},
booktitle={2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC)},
title={Decision Tree Model in the Diagnosis of Breast Cancer},
year={2017},
volume={},
number={},
pages={176-179},
keywords={Decision trees;Breast cancer;Correlation;Shape;Feature extraction;Predictive models;Breast cancer diagnosis;Decision tree;Feature selection},
doi={10.1109/ICCTEC.2017.00046},
ISSN={},
month={Dec},}