Interpreting XGB using LIME and SHAP for otosclerosis and disarticulation diagnosis
- Machine learning algorithms and neural networks have recently been used for the classification of middle ear disorders using wideband acoustic immittance and wideband tympanometry data. This study applies the extreme gradient boosting (XGB) classifier, trained on simulated WAI data, to classify real measured data for normal, otosclerotic, and disarticulated ears. The achieved macro recall of 82 % is comparable to other approaches trained with real measurement data. The interpretability methods LIME and SHAP are used to quantify each feature’s contribution, both revealing energy reflectance between 600-800 Hz as a key feature for all classes. The key feature identified matches the differences that can be visually observed in the training and test data. However, the obtained feature contributions don’t provide enough distinguishable information to recognise incorrect or uncertain classifications.
| Author of HS Reutlingen | Lauxmann, Michael; Winkler, Simon; Sackmann, Benjamin; Priwitzer, Barbara |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-53935 |
| DOI: | https://doi.org/10.1515/cdbme-2024-2166 |
| ISSN: | 2364-5504 |
| Erschienen in: | Current directions in biomedical engineering |
| Publisher: | De Gruyter |
| Place of publication: | Berlin |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2024 |
| Tag: | hearing diagnostic; interpretability; machine learning; middle-ear pathologies; wideband impedance |
| Volume: | 10 |
| Issue: | 4 |
| Page Number: | 5 |
| First Page: | 677 |
| Last Page: | 681 |
| DDC classes: | 570 Biowissenschaften, Biologie |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
