Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology Article Swipe
Ayan Chatterjee
,
Michael A. Riegler
,
Miriam S. Johnson
,
Jishnu Das
,
Nibedita Pahari
,
Raghavendra Ramachandra
,
Bikramaditya Ghosh
,
Arpan Saha
,
Ram Bajpai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-74539-6
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-74539-6
Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-74539-6
- OA Status
- gold
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403425772
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403425772Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-74539-6Digital Object Identifier
- Title
-
Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-10-15Full publication date if available
- Authors
-
Ayan Chatterjee, Michael A. Riegler, Miriam S. Johnson, Jishnu Das, Nibedita Pahari, Raghavendra Ramachandra, Bikramaditya Ghosh, Arpan Saha, Ram BajpaiList of authors in order
- Landing page
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https://doi.org/10.1038/s41598-024-74539-6Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1038/s41598-024-74539-6Direct OA link when available
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Ontology, Computer science, Information retrieval, Data science, Text mining, Statistical analysis, Natural language processing, World Wide Web, Artificial intelligence, Statistics, Mathematics, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.principal | 102 |
| abstract_inverted_index.questions | 84 |
| abstract_inverted_index.reasoning | 169 |
| abstract_inverted_index.responses | 245 |
| abstract_inverted_index.study’s | 263 |
| abstract_inverted_index.symptoms, | 199 |
| abstract_inverted_index.variables | 208 |
| abstract_inverted_index.Challenges | 230 |
| abstract_inverted_index.addressing | 236, 269 |
| abstract_inverted_index.analyzing, | 181 |
| abstract_inverted_index.associated | 40 |
| abstract_inverted_index.challenges | 272 |
| abstract_inverted_index.clustering | 116 |
| abstract_inverted_index.conducted, | 61 |
| abstract_inverted_index.contribute | 4 |
| abstract_inverted_index.correlated | 196 |
| abstract_inverted_index.depression | 42, 257, 284 |
| abstract_inverted_index.depressive | 198 |
| abstract_inverted_index.identified | 193 |
| abstract_inverted_index.mitigating | 243 |
| abstract_inverted_index.mitigation | 282 |
| abstract_inverted_index.selection. | 114 |
| abstract_inverted_index.anonymously | 88 |
| abstract_inverted_index.collecting, | 180 |
| abstract_inverted_index.collection, | 220 |
| abstract_inverted_index.conclusion, | 250 |
| abstract_inverted_index.demographic | 38, 207, 253 |
| abstract_inverted_index.depression. | 21, 30 |
| abstract_inverted_index.development | 19 |
| abstract_inverted_index.distributed | 87 |
| abstract_inverted_index.facilitated | 164 |
| abstract_inverted_index.highlighted | 176 |
| abstract_inverted_index.intensified | 26 |
| abstract_inverted_index.maintaining | 225 |
| abstract_inverted_index.methodology | 264 |
| abstract_inverted_index.multi-class | 139 |
| abstract_inverted_index.population, | 235 |
| abstract_inverted_index.significant | 202 |
| abstract_inverted_index.statistical | 96 |
| abstract_inverted_index.Explanations | 146 |
| abstract_inverted_index.advantageous | 217 |
| abstract_inverted_index.determinants | 28 |
| abstract_inverted_index.explanations | 151 |
| abstract_inverted_index.illustrating | 200 |
| abstract_inverted_index.participants | 66 |
| abstract_inverted_index.predictions. | 156 |
| abstract_inverted_index.representing | 183 |
| abstract_inverted_index.Additionally, | 157 |
| abstract_inverted_index.Interpretable | 144 |
| abstract_inverted_index.pre-processed | 119 |
| abstract_inverted_index.significantly | 3, 255 |
| abstract_inverted_index.understanding | 280 |
| abstract_inverted_index.Model-agnostic | 145 |
| abstract_inverted_index.classification | 140 |
| abstract_inverted_index.post-cleaning) | 70 |
| abstract_inverted_index.representation | 167 |
| abstract_inverted_index.visualization, | 221 |
| abstract_inverted_index.cost-effectiveness | 223 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5063683767 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I200650556 |
| citation_normalized_percentile.value | 0.79678105 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |