Applying machine learning to a virtual serious game for neuropsychological assessment.

Abstract

[EN] Neuropsychological assessment has been traditionally made through paper-and-pencil batteries which usually are time-consuming, decontextualized, and nonecological. These abilities play a critical role in education since they are very related to learning capacity, academic achievement, social functioning, as well as the inhibition of maladaptive behaviors. Meanwhile, serious games are being used in education and psychology to achieve assessments without these limitations, including neuropsychological assessments. While traditional tests can be analyzed with classical statistics, a large number of variables can be extracted from serious games, the analysis of which can be more complex. Machine learning can handle this large amount of information and find patterns that allow us to recognize behaviors. This study aimed to investigate whether machine learning could be used to improve predictive validity in applying a serious game for neuropsychological assessment. Results were based on 60 subjects, including 42 cognitive activities. The validation process showed best results on attention, memory, planning, and cognitive flexibility, achieving accuracies higher or equal to 0.8 and Cohen¿s Kappas higher than 0.55, which implies that the Virtual Serious Game could be a valid tool to perform a neuropsychological evaluation along with traditional tests.