He, C., Gu, Y., Wei, H. orcid.org/0000-0002-4704-7346 et al. (1 more author) (2022) New measurement of the body mass index with bioimpedance using a novel interpretable Takagi-Sugeno Fuzzy NARX predictive model. In: Jiang, R., Zhang, L., Wei, H.L., Crookes, D. and Chazot, P., (eds.) Recent Advances in AI‑enabled Automated Medical Diagnosis. AI4MED 2021 : International Symposium on Artificial Intelligence for Medical Applications, 19-23 Aug 2021, Virtual Conference. Taylor & Francis , pp. 253-267. ISBN 9781032008431
Abstract
Body Mass Index (BMI) is an important and useful indicator for medical diagnoses, accurate monitoring and forecasting of BMI are therefore crucial. However, the current measurement of BMI, which is usually highly correlated with the environmental and individual conditions, is inaccurate. Recent developments of bioelectrical impedance show that there is a great potential to improve the measurement of BMI. In this paper, we propose a novel interpretable Takagi-Sugeno Fuzzy NARX (TSF-NARX) model to predict BMI values from bioimpedance signals and anthropometric factors. The proposed model integrates the Nonlinear Auto Regressive Moving Average with Exogenous Input (NARMAX) method and Takagi-Sugeno fuzzy inference. An obvious novelty and advantage of the proposed method is that it provides a new framework, combining the capabilities of fuzzy inference and NARX representation empowered by nonlinear membership functions. The experimental results show that the TSF-NARX model outperforms other models in prediction accuracy and consistency. More importantly, the model identifies both the key frequency bands and anthropometric factors that highly affect the BMI. The proposed model provides a tool for obtaining accurate, interpretable and robust measurement against the intra and extra uncertainty within the clinical diagnosis.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2022 Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot. This is an author-produced version of a paper subsequently published in Recent Advances in AI-enabled Automated Medical Diagnosis. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Body Mass Index; bioimpedance; Takagi-Sugeno fuzzy logic; NARMAX method |
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: | 01 Nov 2021 07:54 |
Last Modified: | 20 Oct 2023 00:13 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179759 |