Machine Learning Predicts Onset of Dementia PhD Student Annette Spooner, with fellow researchers from CHeBA and the School of Computer Science and Engineering, has undertaken the largest comparison of survival analysis methods to date, to predict the onset of dementia using machine learning.
“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.”
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.
The research compared the performance and stability of ten machine learning algorithms, combined with eight feature selection methods capable of performing predictions of this specific type of clinical data.
There is currently no cure for dementia and no treatment available that can successfully change the course of this disease.
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.
“Machine learning models that can predict the time until a person develops dementia are critical tools in helping our understanding of dementia risks,” said lead author and computer scientist." Annette Spooner
"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.
“Using machine learning, we found that neuropsychology scores are the best predictors for onset of dementia.” 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. DOI: 10.1038/s41598-020-77220-w
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