Related Experiment Video
Updated: Feb 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques
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.
Related Concept Videos
Cognitive Learning
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Avoidance Learning and Learned Helplessness
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Associative Learning
Classical conditioning, also known...
Purposive Learning
Observational Learning
Learning Disabilities
Dyslexia
Dyslexia is a...

