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ROC curves for classification trees

R F Raubertas¹, L E Rodewald, S G Humiston

¹Department of Biostatistics, University of Rochester, New York.

Medical Decision Making : an International Journal of the Society for Medical Decision Making

|April 1, 1994

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Recursive partitioning creates intuitive classification trees for medical diagnosis. Varying misclassification costs generates trees with diverse sensitivity and specificity, forming a curve similar to ROC curves.

Area of Science:

Medical Diagnosis
Biostatistics
Machine Learning

Background:

Combining multiple diagnostic tests or patient characteristics into a decision rule is a significant challenge in medical diagnosis.
Recursive partitioning offers an appealing statistical method due to its easily interpretable classification tree outputs.

Purpose of the Study:

To investigate the impact of misclassification costs on the development of classification trees for medical diagnosis.
To generate a series of classification trees optimized for different ranges of false negative and false positive costs.

Main Methods:

Utilized recursive partitioning to construct classification trees.
Systematically varied the ratio of costs for false negatives versus false positives.
Generated multiple trees, each optimal for specific cost ratios, analyzing their sensitivity and specificity.

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Main Results:

A series of classification trees were produced, each tailored to different misclassification cost ratios.
Each generated tree exhibited distinct levels of sensitivity and specificity.
The resulting combinations of sensitivity and specificity formed a curve analogous to a Receiver Operating Characteristic (ROC) curve.

Conclusions:

Classification trees can be effectively adapted to varying misclassification costs in medical diagnosis.
The generated sensitivity-specificity curve provides a valuable tool for evaluating diagnostic performance under different cost scenarios, similar to ROC analysis.