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Constructed temporal features for longitudinal classification of human ageing data

Ribeiro, Caio, Freitas, Alex (2021) Constructed temporal features for longitudinal classification of human ageing data. In: Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). . pp. 106-112. IEEE E-ISBN 978-1-66540-132-6. (doi:10.1109/ICHI52183.2021.00027) (KAR id:91011)

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https://doi.org/10.1109/ICHI52183.2021.00027

Abstract

Standard classification algorithms ignore the time-related information contained in longitudinal data, as they do not consider the time indexes of the features’ different measurements. Accounting for temporal patterns may improve the algorithms’ performance, when applied to longitudinal data. Representing temporal patterns in the data itself has the advantage that those patterns are generic enough to be used with existing powerful classification algorithms, without requiring the design of new and more complex algorithms to exploit them. In this article, we propose 6 different types of constructed temporal features (3 of them being novel contributions), calculated from the values of the different feature measurements taken over time, and investigate whether adding those constructed temporal features to the original longitudinal dataset improves the classification model’s predictive accuracy. Our experiments involved 20 real-world longitudinal datasets created from a human-ageing study, and showed that the proposed approach of adding the constructed temporal features to the original feature set produced better classifiers overall.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ICHI52183.2021.00027
Additional information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled keywords: machine learning, data mining, longitudinal data, classification, random forests, age-related diseases
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 21 Oct 2021 13:09 UTC
Last Modified: 22 Oct 2021 08:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91011 (The current URI for this page, for reference purposes)
Freitas, Alex: https://orcid.org/0000-0001-9825-4700
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