A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

Abstract Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. There is an urgent need for methods that can overcome these chall...

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Autores principales:	Annette Spooner, Emily Chen, Arcot Sowmya, Perminder Sachdev, Nicole A. Kochan, Julian Trollor, Henry Brodaty
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/241adb643d7845fea517bcfa07bbbc26
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spelling	oai:doaj.org-article:241adb643d7845fea517bcfa07bbbc262021-12-02T16:08:59ZA comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction10.1038/s41598-020-77220-w2045-2322https://doaj.org/article/241adb643d7845fea517bcfa07bbbc262020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77220-whttps://doaj.org/toc/2045-2322Abstract Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. There is an urgent need for methods that can overcome these challenges to model this complex data. At present there is no cure for dementia and no treatment that can successfully change the course of the disease. Machine learning models that can predict the time until a patient develops dementia are important tools in helping understand dementia risks and can give more accurate results than traditional statistical methods when modelling high-dimensional, heterogeneous, clinical data. This work compares the performance and stability of ten machine learning algorithms, combined with eight feature selection methods, capable of performing survival analysis of high-dimensional, heterogeneous, clinical data. We developed models that predict survival to dementia using baseline data from two different studies. The Sydney Memory and Ageing Study (MAS) is a longitudinal cohort study of 1037 participants, aged 70–90 years, that aims to determine the effects of ageing on cognition. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal study aimed at identifying biomarkers for the early detection and tracking of Alzheimer's disease. Using the concordance index as a measure of performance, our models achieve maximum performance values of 0.82 for MAS and 0.93 For ADNI.Annette SpoonerEmily ChenArcot SowmyaPerminder SachdevNicole A. KochanJulian TrollorHenry BrodatyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Annette Spooner Emily Chen Arcot Sowmya Perminder Sachdev Nicole A. Kochan Julian Trollor Henry Brodaty A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
description	Abstract Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. There is an urgent need for methods that can overcome these challenges to model this complex data. At present there is no cure for dementia and no treatment that can successfully change the course of the disease. Machine learning models that can predict the time until a patient develops dementia are important tools in helping understand dementia risks and can give more accurate results than traditional statistical methods when modelling high-dimensional, heterogeneous, clinical data. This work compares the performance and stability of ten machine learning algorithms, combined with eight feature selection methods, capable of performing survival analysis of high-dimensional, heterogeneous, clinical data. We developed models that predict survival to dementia using baseline data from two different studies. The Sydney Memory and Ageing Study (MAS) is a longitudinal cohort study of 1037 participants, aged 70–90 years, that aims to determine the effects of ageing on cognition. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal study aimed at identifying biomarkers for the early detection and tracking of Alzheimer's disease. Using the concordance index as a measure of performance, our models achieve maximum performance values of 0.82 for MAS and 0.93 For ADNI.
format	article
author	Annette Spooner Emily Chen Arcot Sowmya Perminder Sachdev Nicole A. Kochan Julian Trollor Henry Brodaty
author_facet	Annette Spooner Emily Chen Arcot Sowmya Perminder Sachdev Nicole A. Kochan Julian Trollor Henry Brodaty
author_sort	Annette Spooner
title	A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
title_short	A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
title_full	A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
title_fullStr	A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
title_full_unstemmed	A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
title_sort	comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/241adb643d7845fea517bcfa07bbbc26
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A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

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