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Interpretable modelling and visualization of biomedical data
File(s)
Author(s)
Ghosh, S
Baranowski, ES
Biehl, M
Arlt, W
Tino, P
Type
Journal Article
Abstract
Applications of interpretable machine learning (ML) techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the transparency of these models increase trust among application domain experts. Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of ML techniques. In this paper we present a family of prototype-based (PB) interpretable models which are capable of handling these issues. Moreover we propose a strategy of harnessing the power of ensembles while maintaining the intrinsic interpretability of the PB models, by averaging over the model parameter manifolds. All the models were evaluated on a synthetic (publicly available dataset) in addition to detailed analyses of two real-world medical datasets (one publicly available). The models and strategies we introduce address the challenges of real-world medical data, while remaining computationally inexpensive and transparent. Moreover, they exhibit similar or superior in performance compared to alternative techniques.
Date Issued
2025-04-14
Date Acceptance
2025-01-08
Citation
Neurocomputing, 2025, 626
URI
URL
DOI
ISSN
0925-2312
Publisher
Elsevier
Journal / Book Title
Neurocomputing
Volume
626
Copyright Statement
© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
License URL
Subjects
ALGORITHMS
CENTER-OF-MASS
CHAINED EQUATIONS
CLASSIFICATION
Computer Science
Computer Science, Artificial Intelligence
Dimensionality reduction
Dissimilarity learning
DISTANCES
Imbalanced classification
Learning vector quantization
Missing data
MISSING-DATA
MULTIPLE IMPUTATION
Science & Technology
SPHERE
SURFACE
Technology
Visualization
Publication Status
Published
Article Number
129405
Date Publish Online
2025-01-30