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Usually, multivariate discriminant algorithms (e.g., PLS-DA, support vector machine, random forest) are optimized and evaluated using internal figures of merit estimated by cross validation (CV) such as the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity or accuracy to assess its discriminant performance

Rfpermute: Estimate Permutation P-Values for Random Forest Importance Metrics (2022)

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Blood tests (FBC and CRP) and plain film radiographs can be undertaken as first line investigations, often most useful to exclude other differentials