R-C4.5 decision tree model and its applications to health care dataset

By Zheng Yao, Peng Liu, Lei Lei, Junjie Yin. 2005

68 citations recorded by the source corpus.

How researchers see this paper

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Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, background): “Decision trees [36] - due to their easy interpretability - have been quite successfully employed in the healthcare domain [6, 45, 14] and clinicians have embraced it to make informed decisions.”
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation (2018, background): “For instance, decision trees are preferred over DNN in the health care domain for disease diagnosis due to their ease of interpretation [1] [2] [3].”
Interpretable Deep Models for ICU Outcome Prediction (2016, methodology): “Decision trees 14 , due to their ease of interpretation, have been successfully employed in the health care domain 15,16,17 , and clinicians have embraced them for predictive tasks such as disease diagnosis.”
Decision trees: a recent overview (2011, methodology): “One of the strategies proposed is to sort the data using quick sort or counting sort, and the other strategy is to compute the local threshold of C4.5 using main memory version of the Rainforest algorithm, which does not need sorting.”