Evaluating binary classifiers: extending the Efficiency Index

Abstract

Aim: To further develop an ‘efficiency index’ (EI), the ratio of classifier accuracy to inaccuracy, to construct balanced EI (BEI) and unbiased EI (UEI) measures to evaluate a dementia screening test. EI formulations were compared with cognate formulations of the identification index (II). Materials & methods: A prospective pragmatic test accuracy study dataset examining Mini-Addenbrooke's Cognitive Examination (MACE) was used. Results: EI, BEI and UEI varied with test cutoff. UEI was the most stringent measure, correcting for both disease prevalence and test threshold. Unlike II formulations, the boundary values of EI formulations (0,∞) ensure that negative values never occur. Conclusion: EI metrics may be useful for the evaluation of cognitive screening instruments and other diagnostic tests used for neurodegenerative disorders.