Computing Degree Show 2018

Prediction of morphological brain network evolution for late dementia classification

Alzheimer’s Disease(AD) affects millions of people around the globe, with approximately 47 million affected circa 2016. People afflicted by the disease need continuous treatment that may need to be specified to individuals needs as each case can be entirely unique depending on their background, age, values and much more. The costs and resources required for this care are not easily covered, even in high-income countries, and with no known cure the number of people affected by AD will continue to increase with the world’s growing population.


Although difficult to combat the disease at the moment, we can instead prepare for its effects. As such the aim of the developed machine learning algorithm is to accurately predict and classify the chance of an individual developing AD at a certain time point. Using the neonatal features of the brain, which have been extracted from MRI, we are able to see changes and developments of the brain over a period time which are intrinsically linked to the development of AD. 

 The aim of this project is to improve on the previous accuracy rates of similar systems using state of the art prediction methods.

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