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Random forest prediction of Alzheimer’s disease using pairwise selection from time series data

Time-dependent data collected in studies of Alzheimer’s disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to lea...

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Detalles Bibliográficos
Autores principales: Moore, P. J., Lyons, T. J., Gallacher, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375557/
https://www.ncbi.nlm.nih.gov/pubmed/30763336
http://dx.doi.org/10.1371/journal.pone.0211558
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author Moore, P. J.
Lyons, T. J.
Gallacher, J.
author_facet Moore, P. J.
Lyons, T. J.
Gallacher, J.
author_sort Moore, P. J.
collection PubMed
description Time-dependent data collected in studies of Alzheimer’s disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer’s disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.
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spelling pubmed-63755572019-03-01 Random forest prediction of Alzheimer’s disease using pairwise selection from time series data Moore, P. J. Lyons, T. J. Gallacher, J. PLoS One Research Article Time-dependent data collected in studies of Alzheimer’s disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer’s disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods. Public Library of Science 2019-02-14 /pmc/articles/PMC6375557/ /pubmed/30763336 http://dx.doi.org/10.1371/journal.pone.0211558 Text en © 2019 Moore et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Moore, P. J.
Lyons, T. J.
Gallacher, J.
Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
title Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
title_full Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
title_fullStr Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
title_full_unstemmed Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
title_short Random forest prediction of Alzheimer’s disease using pairwise selection from time series data
title_sort random forest prediction of alzheimer’s disease using pairwise selection from time series data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375557/
https://www.ncbi.nlm.nih.gov/pubmed/30763336
http://dx.doi.org/10.1371/journal.pone.0211558
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