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

Sebastian Unger, Zafer Bayram, Laura Anderle, Thomas Ostermann

2024

Abstract

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 heterogeneous 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.

Paper Citation

in Harvard Style

Unger S., Bayram Z., Anderle L. and Ostermann T. (2024). Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 585-592. DOI: 10.5220/0012859100003756

in Bibtex Style

@conference{data24,
author={Sebastian Unger and Zafer Bayram and Laura Anderle and Thomas Ostermann},
title={Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={585-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012859100003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data
SN - 978-989-758-707-8
AU - Unger S.
AU - Bayram Z.
AU - Anderle L.
AU - Ostermann T.
PY - 2024
SP - 585
EP - 592
DO - 10.5220/0012859100003756
PB - SciTePress