A diagnosis model of dementia via machine learning
As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aim...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2022.984894/full |
| _version_ | 1828108969906798592 |
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| author | Ming Zhao Jie Li Liuqing Xiang Zu-hai Zhang Sheng-Lung Peng |
| author_facet | Ming Zhao Jie Li Liuqing Xiang Zu-hai Zhang Sheng-Lung Peng |
| author_sort | Ming Zhao |
| collection | DOAJ |
| description | As the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods. |
| first_indexed | 2024-04-11T10:55:17Z |
| format | Article |
| id | doaj.art-544c0a790a804c5e9883c38a523c2745 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-11T10:55:17Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-544c0a790a804c5e9883c38a523c27452022-12-22T04:28:48ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652022-09-011410.3389/fnagi.2022.984894984894A diagnosis model of dementia via machine learningMing Zhao0Jie Li1Liuqing Xiang2Zu-hai Zhang3Sheng-Lung Peng4School of Computer Science, Yangtze University, Jingzhou, ChinaSchool of Computer Science, Yangtze University, Jingzhou, ChinaSchool of Computer Science, Yangtze University, Jingzhou, ChinaDepartment of Ophthalmology, The First Affiliated Hospital of Yangtze University, Jingzhou, ChinaDepartment of Creative Technologies and Product Design, National Taipei University of Business, Taipei, TaiwanAs the aging population poses serious challenges to families and societies, the issue of dementia has also received increasing attention. Dementia detection often requires a series of complex tests and lengthy questionnaires, which are time-consuming. In order to solve this problem, this article aims at the diagnosis method of questionnaire survey, hoping to establish a diagnosis model to help doctors make a diagnosis through machine learning method, and use feature selection method to select important questions to reduce the number of questions in the questionnaire, so as to reduce medical and time costs. In this article, Clinical Dementia Rating (CDR) is used as the data source, and various methods are used for modeling and feature selection, so as to combine similar attributes in the data set, reduce the categories, and finally use the confusion matrix to judge the effect. The experimental results show that the model established by the bagging method has the best effect, and the accuracy rate can reach 80% of the true diagnosis rate; in terms of feature selection, the principal component analysis (PCA) has the best effect compared with other methods.https://www.frontiersin.org/articles/10.3389/fnagi.2022.984894/fulldementiamachine learningbaggingprincipal component analysisdiagnosis model |
| spellingShingle | Ming Zhao Jie Li Liuqing Xiang Zu-hai Zhang Sheng-Lung Peng A diagnosis model of dementia via machine learning Frontiers in Aging Neuroscience dementia machine learning bagging principal component analysis diagnosis model |
| title | A diagnosis model of dementia via machine learning |
| title_full | A diagnosis model of dementia via machine learning |
| title_fullStr | A diagnosis model of dementia via machine learning |
| title_full_unstemmed | A diagnosis model of dementia via machine learning |
| title_short | A diagnosis model of dementia via machine learning |
| title_sort | diagnosis model of dementia via machine learning |
| topic | dementia machine learning bagging principal component analysis diagnosis model |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2022.984894/full |
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