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Random Forest Classification of Cognitive Impairment Using Digital Tree Drawing Test (dTDT) Data
(2024)
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
Introduction: Drawing tasks are an elementary component of psychological assessment in the evaluation of mental health. With the rise of digitalization not only in psychology but healthcare in general, digital drawing tools (dDTs) have also been developed for this purpose. This scoping review aims at summarizing the state of the art of dDTs available to assess mental health conditions in people above preschool age. Methods: PubMed, PsycInfo, PsycArticles, CINAHL, and Psychology and Behavioral Sciences Collection were searched for dDTs from 2000 onwards. The focus was on dDTs, which not only evaluate the final drawing, but also process data. Results: After applying the search and selection strategy, a total of 37 articles, comprising unique dDTs, remained for data extraction. Around 75 % of these articles were published after 2014 and most of them target adults (86.5 %). In addition, dDTs were mainly used in two areas: tremor detection and assessment of cognitive states, utilizing, for example, the Spiral Drawing Test and the Clock Drawing Test. Conclusion: Early detection of mental diseases is an increasingly important field in healthcare. Through the integration of digital and art based solutions, this area could expand into an interdisciplinary science. This review shows that the first steps in this direction have already been taken and that the possibilities for further research, e.g., on the optimized application of dDTs, are still open.
