Abstract/Summary

In this thesis a novel classification model framework to predict Alzheimer’s Disease is described. In this work a novel brain age feature is proposed, which estimates the biological age of parts of the brain affected by Alzheimer’s Disease. This feature can act as a biomarker for medical professionals which together with age, can make an Alzheimer’s Disease prediction with high performance. In addition to this feature, a novel interpretable classification framework is proposed for prediction of AD which can achieve high classification performance. Also, a novel interpretability index is also proposed which indicates to the medical professionals why such prediction has been made and which input features had the greatest impact on the final output. The brain age Alzheimer’s Disease prediction model is also applied to other type and stages of dementia in a multi-class classification setting as an extension of the work. The results achieved in this thesis in both binary and multi-class classification are comparable to the baseline and relevant previous literature. The binary classification accuracy achieved are 92.84% and 89.74% for female and male subjects respectively.