NARMAX model as a sparse, interpretable and transparent machine learning approach for big medical and healthcare data analysis

Billings, S. and Wei, H. orcid.org/0000-0002-4704-7346 (2019) NARMAX model as a sparse, interpretable and transparent machine learning approach for big medical and healthcare data analysis. In: Proceedings of the 5th IEEE International Conference on Data Science and Systems. 5th IEEE International Conference on Data Science and Systems, 10-12 Aug 2019, Zhangjiajie, China. IEEE , pp. 2743-2750. ISBN 978-1-7281-2059-1

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: machine learning; system identification; data-driven model; time series; forecasting; NARMAX model
Dates:
  • Accepted: 15 May 2019
  • Published (online): 3 October 2019
  • Published: 3 October 2019
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 13:30
Last Modified: 31 Oct 2019 16:44
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00385

Download

Accepted Version


Embargoed until: 3 October 2020

Filename: NARMAX for Medical and Healthcare Data Analysis.pdf

Request a copy

Share / Export

Statistics