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Neural Additive Models for Explainable Heart Attack Prediction

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Book cover Computational Science – ICCS 2022 (ICCS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13352))

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Abstract

Heart attack (HA) is a sudden health disorder when the flow of blood to the heart is blocked, causing damage to the heart. According to the World Health Organization (WHO), heart attack is one of the greatest causes of death and disability globally. Early recognition of the various warning signs of a HA can help reduce the severity. Different machine learning (ML) models have been developed to predict the heart attack. However, patients with arterial hypertension (AH) are especially prone to this disorder and have several features that distinguish them from other groups of patients. We apply these features to develop a special model for people suffering from AH. Moreover, we contribute to this field bringing more transparency to the modelling using interpretable machine learning. We also compare the patterns learned by methods with prior information used in heart attack scales and evaluate their efficiency.

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Acknowledgement

This work was supported by the Ministry of Science and Higher Education of Russian Federation, goszadanie no. 2019-1339.

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Authors and Affiliations

  1. ITMO University, Saint Petersburg, Russia

    Ksenia Balabaeva & Sergey Kovalchuk

Authors
  1. Ksenia Balabaeva
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  2. Sergey Kovalchuk
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Corresponding author

Correspondence to Ksenia Balabaeva .

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Balabaeva, K., Kovalchuk, S. (2022). Neural Additive Models for Explainable Heart Attack Prediction. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_11

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  • DOI: https://doi.org/10.1007/978-3-031-08757-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08756-1

  • Online ISBN: 978-3-031-08757-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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