Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data

  • Early detection and diagnosis of dementia is a major challenge for medical research and practice. Hence, in the last decade, digital drawing tests became popular, showing sometimes even better performance than their paper-and-pencil versions. Combined with machine learning algorithms, these tests are used to differentiate between healthy people and people with mild cognitive impairment (MCI) or early Alzheimer’s disease (eAD), commonly using data from the Clock Drawing Test (CDT). In this investigation, a Random Forest Classification (RF) algorithm is trained on digital Tree Drawing Test (dTDT) data, containing socio-medical information and process data of 86 healthy people, 97 people with MCI, and 74 people with eAD. The results indicate that the binary classification works well for homogeneous groups, as demonstrated by a sensitivity of 0.85 and a specificity of 0.9 (AUC of 0.94). In contrast, the performance of both binary and multiclass classification degrades for groups with het erogeneous characteristics, which is reflected in a sensitivity of 0.91 and 0.29 and a specificity of 0.44 and 0.36 (AUC of 0.74 and 0.65), respectively. Nevertheless, as the early detection of cognitive impairment becomes increasingly important in healthcare, the results could be useful for models that aim for automatic identification
Metadaten
Author:Sebastian Unger, Zafer Bayram, Laura AnderleORCiD, Thomas Ostermann
URL:https://www.scitepress.org/Papers/2024/128591/128591.pdf
DOI:https://doi.org/10.5220/0012859100003756
Parent Title (English):Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA 2024
Document Type:Article
Language:English
Date of Publication (online):2024/09/12
Year of first Publication:2024
Publishing Institution:Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen
Release Date:2024/11/25
Tag:Classification; Cognitive Impairment; Digital Tree Drawing Test; Mental Disorders; Random Forest
First Page:585
Last Page:592
Institutes:Fachbereiche / Informatik und Kommunikation
Licence (German):Creative Commons - Namensnennung - Nicht kommerziell - Keine Bearbeitung
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