There is currently no cure for dementia and no treatment that can successfully change its course. Researchers do not yet have a clear understanding of the risk factors for dementia despite decades of intensive study. The number of people living with dementia worldwide is now around 50 million and that number is forecast to triple by 2050 in the absence of any medical breakthrough. There is an urgent need for methods that can overcome these challenges to model this complex data. 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.ĭata 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 methods. 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. 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. We developed models that predict survival to dementia using baseline data from two different studies. 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. 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.

At present there is no cure for dementia and no treatment that can successfully change the course of the disease. 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.

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