Structured Handwritten Input for Dementia Classification

Published: 01 Sept 2024, Last Modified: 30 Sept 2025MIT EECS Masters ThesisEveryoneCC BY 4.0
Abstract: We explore the use of deep learning to score the Digit Symbol Substitution Test (DSST), a paper-and-pencil behavioral test useful in diagnosing Alzheimer’s. We train a model to classify Alzheimer’s based on the subject’s responses to any one of the 108 queries in the test. We then combine predictions across the test to produce a new classifier that is considerably stronger. We also make an exensive search of architectures and optimization techniques that have proved useful in other settings. The ultimate result is a very strong classifier, with AUC for a response to a single question of 86% and for an overall patient of 97.25%.
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