Sensitivity and specificity of automatic audiological classification using expert-labelled audiological data and Common Audiological Functional Parameters

Abstract

Objective

As a step towards the development of an audiological diagnostic supporting tool employing machine learning methods, this article aims at evaluating the classification performance of different audiological measures as well as Common Audiological Functional Parameters (CAFPAs). CAFPAs are designed to integrate different clinical databases and provide abstract representations of measures.

Design

Classification and evaluation of classification performance in terms of sensitivity and specificity are performed on a data set from a previous study, where statistical models of diagnostic cases were estimated from expert-labelled data.

Study sample

The data set contains 287 cases.

Results

The classification performance in clinically relevant comparison sets of two competing categories was analysed for audiological measures and CAFPAs. It was found that for different audiological diagnostic questions a combination of measures using different weights of the parameters is useful. A set of four to six measures was already sufficient to achieve maximum classification performance which indicates that the measures contain redundant information.

Conclusions

The current set of CAFPAs was confirmed to yield in most cases approximately the same classification performance as the respective optimum set of audiological measures. Overall, the concept of CAFPAs as compact, abstract representation of auditory deficiencies is confirmed.

Keywords:

• Audiological diagnostics
• classification
• ROC analysis
• machine learning

Declaration of interest statement

The authors report no conflicts of interest.

Additional information

Funding

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2177/1 - Project ID 390895286.