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Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects

Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383...

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Autores principales: Velazquez, Matthew, Lee, Yugyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8084194/
https://www.ncbi.nlm.nih.gov/pubmed/33914757
http://dx.doi.org/10.1371/journal.pone.0244773
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author Velazquez, Matthew
Lee, Yugyung
author_facet Velazquez, Matthew
Lee, Yugyung
author_sort Velazquez, Matthew
collection PubMed
description Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.
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spelling pubmed-80841942021-05-06 Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects Velazquez, Matthew Lee, Yugyung PLoS One Research Article Alzheimer’s Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials. Public Library of Science 2021-04-29 /pmc/articles/PMC8084194/ /pubmed/33914757 http://dx.doi.org/10.1371/journal.pone.0244773 Text en © 2021 Velazquez et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Velazquez, Matthew
Lee, Yugyung
Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
title Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
title_full Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
title_fullStr Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
title_full_unstemmed Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
title_short Random forest model for feature-based Alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
title_sort random forest model for feature-based alzheimer’s disease conversion prediction from early mild cognitive impairment subjects
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8084194/
https://www.ncbi.nlm.nih.gov/pubmed/33914757
http://dx.doi.org/10.1371/journal.pone.0244773
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