A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk

Nwegbu, Nnanyelugo, Tirunagari, Santosh ORCID logoORCID: https://orcid.org/0000-0002-9064-1965 and Windridge, David ORCID logoORCID: https://orcid.org/0000-0001-5507-8516 (2022) A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk. Scientific Reports, 12 (1) , 4985. pp. 1-16. ISSN 2045-2322 [Article] (doi:10.1038/s41598-022-08757-1)

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Predictive modeling of clinical data is fraught with challenges arising from the manner in which events are recorded. Patients typically fall ill at irregular intervals and experience dissimilar intervention trajectories. This results in irregularly sampled and uneven length data which poses a problem for standard multivariate tools. The alternative of feature extraction into equal-length vectors via methods like Bag-of-Words (BoW) potentially discards useful information. We propose an approach based on a kernel framework in which data is maintained in its native form: discrete sequences of symbols. Kernel functions derived from the edit distance between pairs of sequences may then be utilized in conjunction with support vector machines to classify the data. Our method is evaluated in the context of the prediction task of determining patients likely to develop type 2 diabetes following an earlier episode of elevated blood pressure of 130/80 mmHg. Kernels combined via multi kernel learning achieved an F1-score of 0.96, outperforming classification with SVM 0.63, logistic regression 0.63, Long Short Term Memory 0.61 and Multi-Layer Perceptron 0.54 applied to a BoW representation of the data. We achieved an F1-score of 0.97 on MKL on external dataset. The proposed approach is consequently able to overcome limitations associated with feature-based classification in the context of clinical data.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 34905
Notes on copyright: © The Author(s) 2022
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Depositing User: David Windridge
Date Deposited: 24 Mar 2022 13:47
Last Modified: 07 Jun 2022 10:26
URI: https://eprints.mdx.ac.uk/id/eprint/34905

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