Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques

Abstract

OBJECTIVE: Alzheimer's disease (AD) is one of the most prevalent diseases among the adult population. The early detection of Mild Cognitive Impairment (MCI), which may trigger AD, is essential to slow down the cognitive decline process. METHODS: This paper presents a suit of serious games that aims at detecting AD and MCI overcoming the limitations of traditional tests, as they are time-consuming, affected by confounding factors that distort the result and usually administered when symptoms are evident and it is too late for preventive measures. The battery, named Panoramix, assesses the main early cognitive markers (i.e., memory, executive functions, attention and gnosias). Regarding its validation, it has been tested with a cohort study of 16 seniors, including AD, MCI and healthy individuals. RESULTS: This first pilot study offered initial evidence about psychometric validity, and more specifically about construct, criterion and external validity. After an analysis using machine learning techniques, findings show a promising 100% rate of success in classification abilities using a subset of three games in the battery. Thus, results are encouraging as all healthy subjects were correctly discriminated from those already suffering AD or MCI. CONCLUSIONS: The solid potential of digital serious games and machine learning for the early detection of dementia processes is demonstrated. Such a promising performance encourages further research to eventually introduce this technique for the clinical diagnosis of cognitive impairment.

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