Structured Handwritten Input for Dementia Classification
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flores-gfm-sm-eecs-2024-thesis.pdf
Description
Thesis PDF
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477.56 KB
Format
Adobe PDF
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675b1be033944eccf4aa3565b0338513
Author(s)
Flores, Gerardo
Advisor(s)
Davis, Randall
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
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 extensive 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%.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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