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Automatic CDT Scoring Using Machine Learning with Interpretable Feature

Published: 01 March 2024 Publication History

Abstract

The Clock Drawing Test (CDT) is a widely used cognitive assessment tool in clinical practice. However, it requires a trained neuropsychologist to evaluate the drawings, and the scoring process may be subjective due to the experience of the neuropsychologist. In this paper, we propose a novel automatic CDT scoring method based on interpretable features using machine learning. First, we use image processing techniques to extract features associated with the scoring guideline. Then, we combine these features as an input vector for training the machine learning classifier. Our experimental results demonstrate that our method achieves an accuracy of 82%, which is superior to that of deep learning methods.

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          ICBBB '24: Proceedings of the 2024 14th International Conference on Bioscience, Biochemistry and Bioinformatics
          January 2024
          79 pages
          ISBN:9798400716768
          DOI:10.1145/3640900
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Published: 01 March 2024

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