Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Eigenvalue-Based Diagnostic Equity Testing: A Random Matrix Framework for Detecting Multi-Dimensional Performance Disparities in Clinical Classifiers. |
| Συγγραφείς: |
Olaniran, Oyebayo Ridwan1 (AUTHOR) olaniran.or@unilorin.edu.ng, Alzahrani, Ali Rashash R.2 (AUTHOR), Alharbi, Mohammed H.3 (AUTHOR), Alharbi, Nada Mohammed Saeed4 (AUTHOR), Alzahrani, Asma Ahmad5 (AUTHOR), Kunle, Saheed Ajibade6 (AUTHOR) |
| Πηγή: |
Mathematics (2227-7390). Jul2026, Vol. 14 Issue 14, p2583. 28p. |
| Θεματικοί όροι: |
*Eigenanalysis, *Algorithmic bias, *Machine learning, *Random matrices, *Diagnostic examinations, *Statistical hypothesis testing |
| Περίληψη: |
Evaluating whether clinical classifiers perform equitably across patient subgroups is a central requirement for the responsible deployment of machine learning in medicine. Conventional approaches test one fairness metric at a time, such as sensitivity, positive predictive value, or area under the receiver operating characteristic curve, and therefore cannot detect disparities that manifest only in the joint structure of a group-specific confusion matrix. We develop a unified hypothesis-testing framework rooted in random matrix theory that compares demographic groups through the L 2 distance between their joint eigenvalue densities, yielding a scalar spectral divergence that is sensitive to every cell of the 2 × 2 confusion matrix simultaneously. We derive the closed-form spectral divergence for Gaussian-approximated eigenvalue densities, prove almost-sure consistency of the empirical estimator via the delta method, and construct an extreme-value (Gumbel) test statistic with family-wise error rate control. Monte Carlo experiments comprising 10,000 replications across balanced, moderately imbalanced, and severely imbalanced group-size regimes show that the spectral test keeps Type I errors close to its nominal level while achieving power exceeding 90 % in complex and multi-dimensional violation scenarios, where the best single-metric competitor reaches at most 63 % . Three clinical benchmark datasets from the UCI Machine Learning Repository utilised include Pima Indians Diabetes ( n = 768 ), Cleveland Heart Disease ( n = 303 ), and Heart Failure Clinical Records ( n = 299 ). Results confirm that the spectral method detects statistically significant ( p < 0.001 ) performance disparities missed by all three conventional tests. These results support eigenvalue-based divergence as a practical, model-agnostic diagnostic equity tool for clinical machine learning audits. [ABSTRACT FROM AUTHOR] |
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