Researchers from the Centre for Healthy Brain Ageing (CHeBA) and the School of Computer Science and Engineering at UNSW Sydney have undertaken the largest comparison of survival analysis methods to date, to predict the onset of dementia using machine learning.

The comparison, published in Nature Scientific Reports, is the first work to apply these methods to CHeBA’s Sydney Memory and Ageing Study and examines the most diverse variety of data in a study on dementia to date.

There is currently no cure for dementia and no treatment available that can successfully change the course of this disease. 

Machine learning models that can predict the time until a person develops dementia are critical tools in helping our understanding of dementia risks.  Using data from the Sydney Memory and Ageing Study we have found we are able to build models that predict the onset of Alzheimer’s disease and other dementias with quite high accuracy.  A technique known as ‘survival analysis’, which predicts the time to an event, such as the diagnosis of a disease, is required to analyse these data and we have used machine learning techniques that have been adapted to handle censored data, rather than the more traditional statistical techniques.
lead author and computer scientist, Annette Spooner

Recent research has shown that different sources of clinical data can provide complementary information about dementia. Integration of multiple sources of data leads to better prediction of cognitive decline.

“Machine learning can give more accurate results than traditional statistical methods when modelling high-dimensional, heterogeneous, clinical data,” said Ms Spooner, whose research was supervised by Professor Arcot Sowmya and assisted by honours student Emily Chen.

Co-author and Co-Director of CHeBA, Professor Perminder Sachdev, said the models they developed predicted survival to dementia using data from Alzheimer’s Disease Neuroimaging Initiative as well as the Sydney Memory and Ageing Study.

Using machine learning, we found that neuropsychology scores are the best predictors for onset of dementia.
Professor Perminder Sachdev, Co-Author and CHeBA Co-Director

Future research through this collaboration will aim to improve the stability of which variables are selected by the models as being the most predictive of dementia.


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