Sun, B., Wang, G. and Wei, H.-L. orcid.org/0000-0002-4704-7346 (Accepted: 2025) NARX-MLP: a hybrid model for accurate interpretable medical data classification. In: 2025 10th International Conference on Machine Learning Technologies (ICMLT 2025). 2025 10th International Conference on Machine Learning Technologies (ICMLT 2025), 23-25 May 2025, Helsinki, Finland. Institute of Electrical and Electronics Engineers (IEEE) (In Press)
Abstract
Machine learning plays a vital role in healthcare, yet medical datasets pose challenges such as nonlinear relationships, high-dimensional features, and the needs for model and result interpretability. We propose an adaptive NARX-MLP classifier, combining NARX with MLP and an adaptive feature selection procedure using L1 regularization. The performance (e.g., accuracy, precision, recall, and F1-score.) of the proposed methods is tested on two datasets: Hepatitis (static) and EEG Eye State (dynamic), to show the superiority of the new method. The selected features by this method can review nonlinear and temporal dependencies and therefore guarantee the capture of complex patterns while maintaining interpretability.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s) |
Keywords: | Machine learning; NARX; MLP; Feature selection; Classification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Funding Information: | Funder Grant number ROYAL SOCIETY IES\R3\183107 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Aug 2025 08:31 |
Last Modified: | 06 Aug 2025 08:31 |
Status: | In Press |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229976 |
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Filename: ICMLT-2025 ID424 Accepted Manuscript.pdf
