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Accuracy of cognitive screening instruments reconsidered: overall, balanced or unbiased accuracy?

Andrew J Larner
Published in: Neurodegenerative disease management (2022)
Aim: To examine three different accuracy metrics for evaluation of cognitive screening instruments: overall correct classification accuracy (Acc), the sum of true positives and negatives divided by the total number tested; balanced accuracy (balanced Acc), half of the sum of sensitivity and specificity; and unbiased accuracy (unbiased Acc), removing biasing effects of random associations between test results and disease prevalence. Materials & methods: Data from a prospective test accuracy study of Mini-Addenbrooke's Cognitive Examination were used to calculate and plot the Acc measures. Results: Each Acc metric resulted in a similar pattern of results across the range of Mini-Addenbrooke's Cognitive Examination cut-offs for diagnosis of both dementia and mild cognitive impairment. Acc and balanced Acc gave more optimistic outcomes (closer to possible maximum value of 1) than unbiased Acc. Conclusion: Unbiased Acc may have advantages over Acc and balanced Acc by removing biasing effects of random associations between test result and disease prevalence.
Keyphrases
  • mild cognitive impairment
  • cognitive decline
  • machine learning
  • risk factors
  • deep learning
  • adipose tissue
  • patient reported outcomes