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Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation.

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    Deep ROC analysis offers a more equitable and informative evaluation of binary classifiers and diagnostic tests. This method provides nuanced performance insights across various risk groups, unlike general or single-threshold measures.

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    Area of Science:

    • Machine Learning
    • Medical Diagnostics
    • Statistical Modeling

    Background:

    • Common performance measures for binary classifiers (e.g., AUC, accuracy) lack depth, evaluating either all thresholds or a single, potentially suboptimal one.
    • Existing metrics like Area Under the Curve (AUC) are too general, while point measures (accuracy, sensitivity, specificity) are too specific and not equitable across all scenarios.
    • There is a need for performance evaluation methods that provide more granular and equitable insights into classifier and diagnostic test performance.

    Purpose of the Study:

    • To introduce Deep ROC analysis, a novel method for evaluating binary classifier and diagnostic test performance.
    • To provide a more equitable and informative assessment by analyzing performance across multiple groups of predicted risk, true positive rate, or false positive rate.
    • To offer a new interpretation of AUC as balanced average accuracy, relevant at the individual level.

    Main Methods:

    • Developed Deep ROC analysis to measure performance within specific groups of predicted risk, true positive rate, or false positive rate.
    • Calculated group AUC, normalized group AUC, and averaged key performance metrics (sensitivity, specificity, PPV, NPV, LRs) within each group.
    • Introduced a new interpretation of AUC as balanced average accuracy and validated the method using three case studies with a Python toolkit.

    Main Results:

    • Deep ROC analysis allows for detailed performance comparisons between groups, overall measures, point measures, and different models.
    • The method provides a more nuanced understanding of classifier behavior across various operating points and risk strata.
    • Case studies demonstrated the utility and practical applicability of the Deep ROC analysis method and its associated Python toolkit.

    Conclusions:

    • Deep ROC analysis offers a superior alternative to traditional performance measures for binary classifiers and diagnostic tests.
    • The method enhances interpretability and provides equitable performance assessment, particularly valuable in medical diagnostics and decision-making.
    • The developed Python toolkit facilitates the implementation and validation of Deep ROC analysis in real-world applications.