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Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases

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XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 (MEDICON 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 76))

Abstract

The problem of classifying subjects into risk categories is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of these algorithms is to predict dichotomous responses (e.g. healthy/at risk) based on several features. Similarly to statistical inference models, also ML models are subject to the common problem of class imbalance. Therefore, they are affected by the majority class increasing the false negative rate.

In this paper, we built and evaluated eighteen ML models classifying approximately 4300 female participants from the UK Biobank into three categorical risk statuses based on responses for the discretised visceral adipose tissue values from magnetic resonance imaging. We also examined the effect of sampling techniques on classification modelling when dealing with class imbalance.

Results showed that the use of sampling techniques had a significant impact. They not only drove an improvement in predicting patients risk status, but also facilitated an increase in the information contained within each variable. Based on domain experts criteria, the three best models for classification were finally identified.

These encouraging results will guide further developments of classification models for predicting visceral adipose tissue without the need for a costly scan.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Westminster, London, W1W 6UW, UK

    Mahmoud Aldraimli & Thierry J. Chaussalet

  2. School of Life Sciences, University of Westminster, London, W1W 6UW, UK

    James Parkinson, Brandon Whitcher, E. Louise Thomas, Jimmy D. Bell & Miriam V. Dwek

  3. School of Computing (Medway), University of Kent, Chatham Maritime, ME4 4AG, UK

    Daniele Soria

Authors
  1. Mahmoud Aldraimli
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  2. Daniele Soria
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  5. E. Louise Thomas
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  7. Thierry J. Chaussalet
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Correspondence to Mahmoud Aldraimli .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Henriques

  2. Universidade do Minho, Braga, Portugal

    Nuno Neves

  3. University of Coimbra, Coimbra, Portugal

    Paulo de Carvalho

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Aldraimli, M. et al. (2020). Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_81

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  • DOI: https://doi.org/10.1007/978-3-030-31635-8_81

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31634-1

  • Online ISBN: 978-3-030-31635-8

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