An improved weighted decision tree approach for breast cancer prediction

Author:

Juneja KapilORCID,Rana Chhavi

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems

Reference25 articles.

1. Huangfu W, Wang F, Liu L, Long K, Lin X (2016) A breast cancer risk classification model based on the features selected by novel F-Score index for the imbalanced multi-feature dataset. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Chengdu, China, pp 198–203

2. Dutra I, Salvini R, Burnside E, Ferreira P (2016) Interpretable models to predict breast cancer. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, pp 1507–1511

3. Ngom A, Rueda L, Huy PQ (2016) A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, pp 1535–1541

4. Cosma G, Brown D, Pockley AG, Alzubaidi A (2016) Breast cancer diagnosis using a hybrid genetic algorithm for feature selection based on mutual information. In: International Conference on Interactive Technologies and Games (ITAG), Notthingham, pp 70–76

5. Rajendran GS, Varsha J, Kavitha KR (2016) A correlation based SVM-recursive multiple feature elimination classifier for breast cancer disease using microarray. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, pp 2677–2683

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