Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games
Mild cognitive impairment (MCI) is an indicative precursor of Alzheimer’s disease and its early detection is critical to restrain further cognitive deterioration through preventive measures. In this context, the capacity of serious games combined with machine learning for MCI detection is examined....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/17/8184 |
| _version_ | 1797521673385148416 |
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| author | Christos Karapapas Christos Goumopoulos |
| author_facet | Christos Karapapas Christos Goumopoulos |
| author_sort | Christos Karapapas |
| collection | DOAJ |
| description | Mild cognitive impairment (MCI) is an indicative precursor of Alzheimer’s disease and its early detection is critical to restrain further cognitive deterioration through preventive measures. In this context, the capacity of serious games combined with machine learning for MCI detection is examined. In particular, a custom methodology is proposed, which consists of a series of steps to train and evaluate classification models that could discriminate healthy from cognitive impaired individuals on the basis of game performance and other subjective data. Such data were collected during a pilot evaluation study of a gaming platform, called COGNIPLAT, with 10 seniors. An exploratory analysis of the data is performed to assess feature selection, model overfitting, optimization techniques and classification performance using several machine learning algorithms and standard evaluation metrics. A production level model is also trained to deal with the issue of data leakage while delivering a high detection performance (92.14% accuracy, 93.4% sensitivity and 90% specificity) based on the Gaussian Naive Bayes classifier. This preliminary study provides initial evidence that serious games combined with machine learning methods could potentially serve as a complementary or an alternative tool to the traditional cognitive screening processes. |
| first_indexed | 2024-03-10T08:15:52Z |
| format | Article |
| id | doaj.art-a6db7f8d11874681826742b67768bf62 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T08:15:52Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a6db7f8d11874681826742b67768bf622023-11-22T10:22:54ZengMDPI AGApplied Sciences2076-34172021-09-011117818410.3390/app11178184Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious GamesChristos Karapapas0Christos Goumopoulos1Information & Communication Systems Engineering Department, University of the Aegean, 83200 Samos, GreeceInformation & Communication Systems Engineering Department, University of the Aegean, 83200 Samos, GreeceMild cognitive impairment (MCI) is an indicative precursor of Alzheimer’s disease and its early detection is critical to restrain further cognitive deterioration through preventive measures. In this context, the capacity of serious games combined with machine learning for MCI detection is examined. In particular, a custom methodology is proposed, which consists of a series of steps to train and evaluate classification models that could discriminate healthy from cognitive impaired individuals on the basis of game performance and other subjective data. Such data were collected during a pilot evaluation study of a gaming platform, called COGNIPLAT, with 10 seniors. An exploratory analysis of the data is performed to assess feature selection, model overfitting, optimization techniques and classification performance using several machine learning algorithms and standard evaluation metrics. A production level model is also trained to deal with the issue of data leakage while delivering a high detection performance (92.14% accuracy, 93.4% sensitivity and 90% specificity) based on the Gaussian Naive Bayes classifier. This preliminary study provides initial evidence that serious games combined with machine learning methods could potentially serve as a complementary or an alternative tool to the traditional cognitive screening processes.https://www.mdpi.com/2076-3417/11/17/8184mild cognitive impairmentserious gamesmachine learningfeature selectiondata transformationsclassification |
| spellingShingle | Christos Karapapas Christos Goumopoulos Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games Applied Sciences mild cognitive impairment serious games machine learning feature selection data transformations classification |
| title | Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games |
| title_full | Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games |
| title_fullStr | Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games |
| title_full_unstemmed | Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games |
| title_short | Mild Cognitive Impairment Detection Using Machine Learning Models Trained on Data Collected from Serious Games |
| title_sort | mild cognitive impairment detection using machine learning models trained on data collected from serious games |
| topic | mild cognitive impairment serious games machine learning feature selection data transformations classification |
| url | https://www.mdpi.com/2076-3417/11/17/8184 |
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