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Methods Inf Med 2018; 57(04): 197-207
DOI: 10.3414/ME17-02-0011
Focus Theme – Original Articles
Georg Thieme Verlag KG Stuttgart · New York

Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques[*]

A Preliminary Study
Sonia Valladares-Rodriguez
1   Department of Telematic Engineering, University of Vigo, Vigo, Pontevedra, Spain
,
Roberto Pérez-Rodriguez
1   Department of Telematic Engineering, University of Vigo, Vigo, Pontevedra, Spain
,
J. Manuel Fernandez-Iglesias
1   Department of Telematic Engineering, University of Vigo, Vigo, Pontevedra, Spain
,
Luis E. Anido-Rifón
1   Department of Telematic Engineering, University of Vigo, Vigo, Pontevedra, Spain
,
David Facal
2   Department of Developmental Psychology, Facultade de Psicoloxia, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
,
Carlos Rivas-Costa
1   Department of Telematic Engineering, University of Vigo, Vigo, Pontevedra, Spain
› Author Affiliations Funding The present work was partially funded by the government of Galicia (Xunta de Galicia, Spain), which covered the travel expenses to participants’ homes during the pilot study (grant # 2016/236), and also by ‘Rede Galega de Investigación en Demencias’ (IN607C2017/02) funded by Axencia Galega de Innovación GAIN – Xunta de Galicia.
Further Information

Publication History

received: 28 July 2017

accepted: 06 July 2018

Publication Date:
24 September 2018 (online)

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Summary

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.

Key words

Alzheimer’s disease - early detection - machine learning - data analytics - serious games - Classification and Regression Trees - logistic regression - random forest - Support Vector Machine

* Supplementary material published on our website https://doi.org/10.3414/ME17-02-0011


 

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