Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort

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Stamate, Daniel; Musto, Henry; Ajnakina, Olesya and Stahl, Daniel. 2022. 'Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort'. In: 18th IFIP International Conference on Artificial Intelligence Applications and Innovations - AIAI 2022. Hersonissos, Crete, Greece 17 - 20 June 2022. [Conference or Workshop Item]

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Official URL: https://doi.org/10.1007/978-3-031-08341-9_35

Abstract or Description

Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algorithms of Random Forest and Elastic Net are applied to train, optimise, and validate predictive models based on the English Longitudinal Study of Ageing – ELSA cohort. The two survival machine learning models are compared with the conventional statistical Cox proportional hazard model, proving their superior predictive capability and stability on the ELSA data, as demonstrated by computationally intensive procedures such as nested cross-validation and Monte Carlo validation. This study is the first to apply survival machine learning to the ELSA data, and demonstrates in this case the superiority of AI based predictive modelling approaches over the widely employed Cox statistical approach in survival analysis. Implications, methodological considerations, and future research directions are discussed.

Item Type:

Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.1007/978-3-031-08341-9_35

Additional Information:

“This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-08341-9_35. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms”.

Daniel Stamate is part-funded by Alzheimer’s Research UK (ARUK-PRRF2017-012), the University of Manchester, and Goldsmiths College, University of London. Daniel Stahl is part-funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.

Keywords:

Predicting risk of dementia, Survival machine learning, Survival random forests, Survival elastic net, Cox proportional hazard, Nested crossvalidation, Monte Carlo validation

Departments, Centres and Research Units:

Computing

Dates: