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Comparative Study
. 2020 Nov 23;10(1):20410.
doi: 10.1038/s41598-020-77220-w.

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

Annette Spooner  1 , Emily Chen  2 , Arcot Sowmya  2 , Perminder Sachdev  3   4 , Nicole A Kochan  4 , Julian Trollor  3   4   5 , Henry Brodaty  3   4
Affiliations
Comparative Study

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

Annette Spooner et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Heatmap illustrating the performance of each of the machine learning algorithms with each feature selection method on the MAS dataset, measured by the mean concordance index.
Figure 2
Figure 2
Heatmap illustrating the performance of each of the machine learning algorithms with each feature selection method on the ADNI dataset, measured by the mean concordance index.
Figure 3
Figure 3
Average number of features selected by each model—MAS dataset.
Figure 4
Figure 4
Average number of features selected by each model—ADNI dataset.
See this image and copyright information in PMC

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