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New measurement of the body mass index with bioimpedance using a novel interpretable Takagi-Sugeno Fuzzy NARX predictive model

chapter
posted on 09.09.2021, 10:48 by Changjiang He, Yuanlin Gu, Hua-Liang Wei, Qinggang MengQinggang Meng
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 (TSFNARX) 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.

Funding

Innovate UK under grant reference 26526

History

School

  • Science

Department

  • Computer Science

Published in

Recent Advances in AI-enabled Automated Medical Diagnosis

Publisher

Routledge

Version

AM (Accepted Manuscript)

Publisher statement

This is an Accepted Manuscript of a book chapter published by Routledge in Recent Advances in AI-enabled Automated Medical Diagnosis on [date of publication], available online: http://www.routledge.com/[BOOK ISBN URL].

Language

en

Depositor

Prof Qinggang Meng. Deposit date: 6 September 2021

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