3 research outputs found
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
Three-dimensional clinical gait analysis is essential for selecting optimal
treatment interventions for patients with cerebral palsy (CP), but generates a
large amount of time series data. For the automated analysis of these data,
machine learning approaches yield promising results. However, due to their
black-box nature, such approaches are often mistrusted by clinicians. We
propose gaitXplorer, a visual analytics approach for the classification of
CP-related gait patterns that integrates Grad-CAM, a well-established
explainable artificial intelligence algorithm, for explanations of machine
learning classifications. Regions of high relevance for classification are
highlighted in the interactive visual interface. The approach is evaluated in a
case study with two clinical gait experts. They inspected the explanations for
a sample of eight patients using the visual interface and expressed which
relevance scores they found trustworthy and which they found suspicious.
Overall, the clinicians gave positive feedback on the approach as it allowed
them a better understanding of which regions in the data were relevant for the
classification.Comment: 7 pages, 4 figures; supplemental material 9 pages, 8 figures; to be
published in the proceedings of the 2022 IEEE Workshop on TRust and EXpertise
in Visual Analytics (TREX
-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers
The protection of private information is a crucial issue in data-driven
research and business contexts. Typically, techniques like anonymisation or
(selective) deletion are introduced in order to allow data sharing, \eg\ in the
case of collaborative research endeavours. For use with anonymisation
techniques, the -anonymity criterion is one of the most popular, with
numerous scientific publications on different algorithms and metrics.
Anonymisation techniques often require changing the data and thus necessarily
affect the results of machine learning models trained on the underlying data.
In this work, we conduct a systematic comparison and detailed investigation
into the effects of different -anonymisation algorithms on the results of
machine learning models. We investigate a set of popular -anonymisation
algorithms with different classifiers and evaluate them on different real-world
datasets. Our systematic evaluation shows that with an increasingly strong
-anonymity constraint, the classification performance generally degrades,
but to varying degrees and strongly depending on the dataset and anonymisation
method. Furthermore, Mondrian can be considered as the method with the most
appealing properties for subsequent classification.Comment: 42 pages, 27 figure