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Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort

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Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14162))

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Abstract

The rise of Alzheimer’s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer’s Disease risk.

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Acknowledgements

Daniel Stahl was part funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. This study represents independent research and views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

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Authors and Affiliations

  1. Data Science and Soft Computing Lab, Computing Department, Goldsmiths College, University of London, London, UK

    Henry Musto, Daniel Stamate & Ida Pu

  2. Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, University of Manchester, Manchester, UK

    Daniel Stamate

  3. Institute of Psychiatry Psychology and Neuroscience, Biostatistics and Health Infomatics Department, King’s College London, London, UK

    Daniel Stahl

Authors
  1. Henry Musto
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  2. Daniel Stamate
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  3. Ida Pu
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  4. Daniel Stahl
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Corresponding author

Correspondence to Henry Musto .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. Universität Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Musto, H., Stamate, D., Pu, I., Stahl, D. (2023). Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_53

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  • DOI: https://doi.org/10.1007/978-3-031-41456-5_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41455-8

  • Online ISBN: 978-3-031-41456-5

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