Early dementia detection with speech analysis and machine learning techniques
Abstract This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-04-01
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| Series: | Discover Sustainability |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43621-024-00217-2 |
| _version_ | 1797209574040666112 |
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| author | Zerin Jahan Surbhi Bhatia Khan Mo Saraee |
| author_facet | Zerin Jahan Surbhi Bhatia Khan Mo Saraee |
| author_sort | Zerin Jahan |
| collection | DOAJ |
| description | Abstract This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection. |
| first_indexed | 2024-04-24T09:56:51Z |
| format | Article |
| id | doaj.art-97f665c80d114d7f91d99e2d03e7d940 |
| institution | Directory Open Access Journal |
| issn | 2662-9984 |
| language | English |
| last_indexed | 2024-04-24T09:56:51Z |
| publishDate | 2024-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj.art-97f665c80d114d7f91d99e2d03e7d9402024-04-14T11:06:03ZengSpringerDiscover Sustainability2662-99842024-04-015111810.1007/s43621-024-00217-2Early dementia detection with speech analysis and machine learning techniquesZerin Jahan0Surbhi Bhatia Khan1Mo Saraee2School of Science, Engineering and Environment, University of SalfordSchool of Science, Engineering and Environment, University of SalfordSchool of Science, Engineering and Environment, University of SalfordAbstract This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection.https://doi.org/10.1007/s43621-024-00217-2DementiaSpeech transcript analysisText miningFeature extractionWord embeddingMachine learning |
| spellingShingle | Zerin Jahan Surbhi Bhatia Khan Mo Saraee Early dementia detection with speech analysis and machine learning techniques Discover Sustainability Dementia Speech transcript analysis Text mining Feature extraction Word embedding Machine learning |
| title | Early dementia detection with speech analysis and machine learning techniques |
| title_full | Early dementia detection with speech analysis and machine learning techniques |
| title_fullStr | Early dementia detection with speech analysis and machine learning techniques |
| title_full_unstemmed | Early dementia detection with speech analysis and machine learning techniques |
| title_short | Early dementia detection with speech analysis and machine learning techniques |
| title_sort | early dementia detection with speech analysis and machine learning techniques |
| topic | Dementia Speech transcript analysis Text mining Feature extraction Word embedding Machine learning |
| url | https://doi.org/10.1007/s43621-024-00217-2 |
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