Key result
CART methodology underperforms previously used models for diagnosing anterior chest pain.
Why the study?
Does CART methodology improve diagnostic classification in patients with anterior chest pain compared to correspondence analysis and independent Bayes classification?
Does CART methodology improve diagnostic classification in patients with anterior chest pain compared to correspondence analysis and independent Bayes classification?
While CART methodology can identify important indicators and cutpoints for continuous variables in diagnosing anterior chest pain, its overall classification performance is disappointing.
CART performance cautions against adoption for anterior chest pain diagnosis; leaves open refinements or applications in other settings.
The use of classification and regression tree (CART) methodology is explored for the diagnosis of patients complaining of anterior chest pain. The results are compared with those previously obtained using correspondence analysis and independent Bayes classification. The technique is shown to be of potential value for identifying important indicators and cutpoints for continuous variables, although the overall classification performance was rather disappointing. Suggestions are made for extensions to the methodology to make it more suitable for clinical practice.
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Crichton et al. (1997) studied Anterior chest pain. Classification and regression tree (CART) methodology vs. Correspondence analysis and independent Bayes classification was evaluated on Overall classification performance for diagnosis. Classification and regression tree (CART) methodology yielded disappointing overall classification performance for diagnosing anterior chest pain compared to previously used models.
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