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Title:
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SCREENING OF ALZHEIMER'S DISEASE AND MILD COGNITIVE IMPAIRMENT THROUGH INTEGRATED ON-LINE AND OFF-LINE HOUSE DRAWING TESTS |
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Author(s):
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Nina Hosseini-Kivanani, Elena Salobrar-García, Lorena Elvira-Hurtado, Mario Salas,
Christoph Schommer and Luis A. Leiva |
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ISBN:
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978-989-8704-62 |
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Editors:
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Paula Miranda and Pedro Isaías |
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Year:
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2024 |
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Edition:
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Single |
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Keywords:
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On-Line, Off-Line, Deep Learning, Alzheimer's Disease, Mild Cognitive Impairment, House Drawing |
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Type:
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Full |
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First Page:
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203 |
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Last Page:
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209 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Objective: Evaluate the effectiveness of machine learning (ML) algorithms in classifying mild cognitive impairment (MCI)
and Alzheimer's disease (AD) using features derived from the House Drawing Test (HDT). Methods: The HDT was
administered to 58 participants, categorized into AD (n = 22), MCI (n= 25), and Healthy Controls (HC, n = 11). Drawings
were simultaneously captured using an electronic pen (on-line format) and scanned (off-line format). Results: The models
achieved high classification accuracy across all groups: HC vs. MCI (67%), MCI vs. AD (70%), HC vs. AD (76%). Our
results showcase the potential of ML for early screening of neurodegenerative diseases |
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