Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression

By S. Lemon, Jason Roy, M. Clark, P. Friedmann, W. Rakowski. Annals of Behavioral Medicine, 2003

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Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types (2013, methodology): “Furthermore, it has been suggested that classification and regression tree methods are adept at identifying important interactions in the data [8e10] and in identifying clinical subgroups of subjects at very high or very low risk of adverse outcomes [11].”
Salivary Transcriptome Diagnostics for Oral Cancer Detection (2004, methodology): “CART fits the classification model by binary recursive partitioning, in which each step involves searching for the predictor variable that results in the best split of the cancer versus the normal groups (24).”
A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending (2011, methodology): “However, conventional regression models (including LR) are mostly used to determine the average effect of independent variables on the dependent variable ( Lemon et al. 2003 ), which tends to lean in the direction of the average members of the population, without consideration of the special needs o”