AdaBoost Algorithm with Random Forests for Predicting Breast Cancer Survivability

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Thongkam, Jaree, Xu, Guandong and Zhang, Yanchun (2008) AdaBoost Algorithm with Random Forests for Predicting Breast Cancer Survivability. In: IEEE International Joint Conference on Neural Networks, 2008 : IJCNN 2008 (IEEE World Congress on Computational Intelligence) : 1-8 June, 2008. IEEE, Piscataway, New Jersey, pp. 3062-3069.


In this paper we propose a combination of the AdaBoost and random forests algorithms for constructing a breast cancer survivability prediction model. We use random forests as a weak learner of AdaBoost for selecting the high weight instances during the boosting process to improve accuracy, stability and to reduce overfitting problems. The capability of this hybrid method is evaluated using basic performance measurements (e.g., accuracy, sensitivity, and specificity), Receiver Operating Characteristic (ROC) curve and Area Under the receiver operating characteristic Curve (AUC). Experimental results indicate that the proposed method outperforms a single classifier and other combined classifiers for the breast cancer survivability prediction. --Conference held in Hong Kong

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Item type Book Section
DOI 10.1109/IJCNN.2008.4634231
Official URL
ISBN 9781424418206 (print) 9781424418213 (online) 9781424418213
Subjects Historical > FOR Classification > 1117 Public Health and Health Services
Historical > SEO Classification > 8903 Information Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID14757, educational technologies, artificial intelligence, cancer, medical computing, sensitivity analysis, stability, prediction, predictive models, overfitting problems, data mining, decision trees, diseases, mathematics, medical diagnostic imaging, mathematical models, signal processing algorithms, support vector machines
Citations in Scopus 55 - View on Scopus
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