Machine learning for the life-time risk prediction of Alzheimer’s disease: a systematic review

Rowe, Thomas, Katzourou, Ioanna, Stevenson-Hoare, Joshua, Bracher-Smith, Matthew, Ivanov, Dobril ORCID: https://orcid.org/0000-0001-6271-6301 and Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483 2021. Machine learning for the life-time risk prediction of Alzheimer’s disease: a systematic review. Brain Communications 3 (4) , fcab246. 10.1093/braincomms/fcab246

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Abstract

Alzheimer’s disease is a neurodegenerative disorder and the most common form of dementia. Early diagnosis may assist interventions to delay onset and reduce the progression rate of the disease. We systematically reviewed the use of machine learning algorithms for predicting Alzheimer’s disease using single nucleotide polymorphisms and instances where these were combined with other types of data. We evaluated the ability of machine learning models to distinguish between controls and cases, while also assessing their implementation and potential biases. Articles published between December 2009 and June 2020 were collected using Scopus, PubMed and Google Scholar. These were systematically screened for inclusion leading to a final set of 12 publications. Eighty-five per cent of the included studies used the Alzheimer's Disease Neuroimaging Initiative dataset. In studies which reported area under the curve, discrimination varied (0.49–0.97). However, more than half of the included manuscripts used other forms of measurement, such as accuracy, sensitivity and specificity. Model calibration statistics were also found to be reported inconsistently across all studies. The most frequent limitation in the assessed studies was sample size, with the total number of participants often numbering less than a thousand, whilst the number of predictors usually ran into the many thousands. In addition, key steps in model implementation and validation were often not performed or unreported, making it difficult to assess the capability of machine learning models.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Biosciences MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Medicine
Additional Information:	This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/)
Publisher:	Oxford University Press
ISSN:	2632-1297
Funders:	MRC, Wellcome Trust
Date of First Compliant Deposit:	18 October 2021
Date of Acceptance:	17 August 2021
Last Modified:	23 Jul 2024 16:06
URI:	https://orca.cardiff.ac.uk/id/eprint/144902

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