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In this paper a custom classification algorithm based\ud on linear discriminant analysis and probability-based weights\ud is implemented and applied to the hippocampus measurements\ud of structural magnetic resonance images from healthy subjects\ud and Alzheimer’s Disease sufferers; and then attempts to diagnose\ud them as accurately as possible. The classifier works by classifying\ud each measurement of a hippocampal volume as healthy controlsized\ud or Alzheimer’s Disease-sized, these new features are then\ud weighted and used to classify the subject as a healthy control\ud or suffering from Alzheimer’s Disease. The preliminary results\ud obtained reach an accuracy of 85.8% and this is a similar\ud accuracy to state-of-the-art methods such as a Naive Bayes\ud classifier and a Support Vector Machine. An advantage of the\ud method proposed in this paper over the aforementioned state of the art \ud classifiers is the descriptive ability of the classifications\ud it produces. The descriptive model can be of great help to aid a\ud doctor in the diagnosis of Alzheimer’s Disease, or even further the\ud understand of how Alzheimer’s Disease affects the hippocampus.

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