Digital phenotyping for classification of anxiety severity during COVID-19
COVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.877762/full |
| _version_ | 1811240710804865024 |
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| author | Binh Nguyen Martin Ivanov Venkat Bhat Venkat Bhat Sri Krishnan |
| author_facet | Binh Nguyen Martin Ivanov Venkat Bhat Venkat Bhat Sri Krishnan |
| author_sort | Binh Nguyen |
| collection | DOAJ |
| description | COVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2 and CPSS4, which were collected in May and July of 2020, respectively. The survey data consist of up to 102 questions. This work proposes the use of the survey data characteristics to identify the level of anxiety within the Canadian population during the first- and second-phases of COVID-19 and is validated by using the General Anxiety Disorder (GAD)-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top features to represent user anxiety, and support vector machine (SVM) is used to classify the separation of anxiety severity. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77±0.13% and 97.35±0.11% for CPSS2 and CPSS4, respectively. After analysis, we compared the results of the first and second phases of COVID-19 and determined a subset of the features that could be represented as pseudo passive (PP) data. The accurate classification provides a proxy on the potential onsets of anxiety to provide tailored interventions. Future works can augment the proposed PP data for carrying out a more detailed digital phenotyping. |
| first_indexed | 2024-04-12T13:25:06Z |
| format | Article |
| id | doaj.art-559b2f999ce94bb99a4b9c303bc67eb0 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-04-12T13:25:06Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-559b2f999ce94bb99a4b9c303bc67eb02022-12-22T03:31:21ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-10-01410.3389/fdgth.2022.877762877762Digital phenotyping for classification of anxiety severity during COVID-19Binh Nguyen0Martin Ivanov1Venkat Bhat2Venkat Bhat3Sri Krishnan4Signal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaSignal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaSignal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaInterventional Psychiatry Program, St. Michael’s Hospital, Department of Psychiatry, University of Toronto, Toronto, ON, CanadaSignal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaCOVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2 and CPSS4, which were collected in May and July of 2020, respectively. The survey data consist of up to 102 questions. This work proposes the use of the survey data characteristics to identify the level of anxiety within the Canadian population during the first- and second-phases of COVID-19 and is validated by using the General Anxiety Disorder (GAD)-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top features to represent user anxiety, and support vector machine (SVM) is used to classify the separation of anxiety severity. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77±0.13% and 97.35±0.11% for CPSS2 and CPSS4, respectively. After analysis, we compared the results of the first and second phases of COVID-19 and determined a subset of the features that could be represented as pseudo passive (PP) data. The accurate classification provides a proxy on the potential onsets of anxiety to provide tailored interventions. Future works can augment the proposed PP data for carrying out a more detailed digital phenotyping.https://www.frontiersin.org/articles/10.3389/fdgth.2022.877762/fulldigital phenotypingmachine learningCOVID-19anxietymental health |
| spellingShingle | Binh Nguyen Martin Ivanov Venkat Bhat Venkat Bhat Sri Krishnan Digital phenotyping for classification of anxiety severity during COVID-19 Frontiers in Digital Health digital phenotyping machine learning COVID-19 anxiety mental health |
| title | Digital phenotyping for classification of anxiety severity during COVID-19 |
| title_full | Digital phenotyping for classification of anxiety severity during COVID-19 |
| title_fullStr | Digital phenotyping for classification of anxiety severity during COVID-19 |
| title_full_unstemmed | Digital phenotyping for classification of anxiety severity during COVID-19 |
| title_short | Digital phenotyping for classification of anxiety severity during COVID-19 |
| title_sort | digital phenotyping for classification of anxiety severity during covid 19 |
| topic | digital phenotyping machine learning COVID-19 anxiety mental health |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.877762/full |
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