TY  - JOUR
A1  - Unger, Sebastian
A1  - Bayram, Zafer
A1  - Anderle, Laura
A1  - Ostermann, Thomas
T1  - Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data
T2  - Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA 2024
N2  - 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
KW  - Digital Tree Drawing Test, Cognitive Impairment, Mental Disorders, Classification, Random Forest
Y1  - 2024
UR  - https://whge.opus.hbz-nrw.de/frontdoor/index/index/docId/4602
UR  - https://www.scitepress.org/Papers/2024/128591/128591.pdf
SP  - 585
EP  - 592
ER  -