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

Topic: Risk, prevention and care in population studies

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.

Key findings

• 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.

Why it matters

This study compared machine learning methods for survival analysis to predict time until dementia using clinical data. It notes there is no cure for dementia and no treatment that can change its course, and the work is about prediction models, not treatment or prevention.

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Study details

• Study designcohort study
• Participants1,037
• Evidence strengthModerate

Read the original

• DOI: 10.1038/s41598-020-77220-w
• PubMed: 33230128
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{spooner2020-33230128,
  author  = {Spooner, A. and Chen, E. and Sowmya, A. and Sachdev, P. and Kochan, N. A. and Trollor, J. and Brodaty, H.},
  title   = {{A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction}},
  journal = {Scientific reports},
  year    = {2020},
  doi     = {10.1038/s41598-020-77220-w},
  pmid    = {33230128},
  url     = {https://doi.org/10.1038/s41598-020-77220-w}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70-90 years International psychogeriatrics · 2010-07-19 · usefulness score 62/100 · Mild cognitive impairment
• A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages NeuroImage · 2017-04-13 · usefulness score 56/100 · Mild cognitive impairment
• The Alzheimer's disease neuroimaging initiative: progress report and future plans Alzheimer's & dementia : the journal of the Alzheimer's Association · 2010-05-01 · usefulness score 54/100 · Mild cognitive impairment
• Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1995-01-01 · usefulness score 52/100 · Laboratory science
• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests BMC research notes · 2011-08-17 · usefulness score 47/100 · Digital health tools

Later studies that cite this one

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• Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study Lancet regional health. Americas · 2022-11-03 · usefulness score 62/100 · Digital health tools
• Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023-08-31 · usefulness score 56/100 · Digital health tools
• Multi-feature computational framework for combined signatures of dementia in underrepresented settings Journal of neural engineering · 2022-08-25 · usefulness score 48/100 · Digital health tools
• Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems Scientific Reports · 2025-05-13 · usefulness score 44/100 · Digital health tools
• Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques Scientific Reports · 2025-07-09 · usefulness score 40/100 · Getting a diagnosis

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• Trends in Relative Incidence and Prevalence of Dementia Across Non-Hispanic Black and White Individuals in the United States, 2000-2016 JAMA neurology · 2021-03-01 · usefulness score 59/100 · Disparities

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