Wei, H. orcid.org/0000-0002-4704-7346 (2019) Sparse, interpretable and transparent predictive model identification for healthcare data analysis. In: Rojas, I., Joya, G. and Catala, A., (eds.) Proceedings of the 2019 International Work-Conference on Artificial Neural Networks (Advances in Computational Intelligence). 2019 International Work-Conference on Artificial Neural Networks (Advances in Computational Intelligence), 12-14 Jun 2019, Gran Canaria, Spain. Lecture Notes in Computer Science, 15 (11506). Springer , pp. 103-114. ISBN 9783030205201
Abstract
Data-driven modelling approaches play an indispensable role in analyzing and understanding complex processes. This study proposes a type of sparse, interpretable and transparent (SIT) machine learning model, which can be used to understand the dependent relationship of a response variable on a set of potential explanatory variables. An ideal candidate for such a SIT representation is the well-known NARMAX (nonlinear autoregressive moving average with exogenous inputs) model, which can be established from measured input and output data of the system of interest, and the final refined model is usually simple, parsimonious and easy to interpret. The performance of the proposed SIT models is evaluated through two real healthcare datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: | |
Editors: |
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Copyright, Publisher and Additional Information: | © 2019 Springer. This is an author-produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | System identification; Data-driven modelling; Prediction; Healthcare; Machine learning; NARMAX |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 May 2019 14:19 |
Last Modified: | 16 May 2020 00:38 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-20521-8_9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146278 |