Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection
Abstract The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encom...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2023-10-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44230-023-00049-9 |
| _version_ | 1797376914245025792 |
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| author | K. M. Aslam Uddin Farida Siddiqi Prity Maisha Tasnim Sumiya Nur Jannat Mohammad Omar Faruk Jahirul Islam Saydul Akbar Murad Apurba Adhikary Anupam Kumar Bairagi |
| author_facet | K. M. Aslam Uddin Farida Siddiqi Prity Maisha Tasnim Sumiya Nur Jannat Mohammad Omar Faruk Jahirul Islam Saydul Akbar Murad Apurba Adhikary Anupam Kumar Bairagi |
| author_sort | K. M. Aslam Uddin |
| collection | DOAJ |
| description | Abstract The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encompass densely populated areas, adherence to mask-wearing protocols, inadequate awareness levels, and various behavioral and social practices. Despite the extensive research surrounding COVID-19 detection, an unfortunate dearth of studies has emerged to meticulously evaluate the intricate interplay between socio-demographic and behavioral factors and the likelihood of COVID-19 infection. Thus, a comprehensive online-based cross-sectional survey was methodically orchestrated, amassing data from a substantial sample size of 500 respondents. The precisely designed survey questionnaire encompassed various variables encompassing socio-demographics, behaviors, and social factors. The Bivariate Pearson’s Chi-square association test was deftly employed to unravel the complex associations between the explanatory variables and COVID-19 infection. The feature importance approach was also introduced to discern the utmost critical features underpinning this infectious predicament. Four distinct Machine Learning (ML) algorithms, specifically Decision Tree, Random Forest, CatBoost, and XGBoost, were employed to accurately predict COVID-19 infection based on a comprehensive analysis of socio-demographic and behavioral factors. The performance of these models was rigorously assessed using a range of evaluation metrics, including accuracy, recall, precision, ROC-AUC score, and F1 score. Pearson’s Chi-square test revealed a statistically significant association between vaccination status and COVID-19 infection. The use of sanitizer and masks, the timing of infection, and the interval between the first and second vaccine doses were significantly correlated with the likelihood of contracting the COVID-19 virus. Among the ML models tested, the XGBoost classifier demonstrated the highest classification accuracy, achieving an impressive 97.6%. These findings provide valuable insights for individuals, communities, and policymakers to implement targeted strategies aimed at mitigating the impact of the COVID-19 pandemic. |
| first_indexed | 2024-03-08T19:46:24Z |
| format | Article |
| id | doaj.art-790f8169838c444ea4f978ece67aa2f2 |
| institution | Directory Open Access Journal |
| issn | 2667-1336 |
| language | English |
| last_indexed | 2024-03-08T19:46:24Z |
| publishDate | 2023-10-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj.art-790f8169838c444ea4f978ece67aa2f22023-12-24T12:20:56ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362023-10-013444146010.1007/s44230-023-00049-9Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 DetectionK. M. Aslam Uddin0Farida Siddiqi Prity1Maisha Tasnim2Sumiya Nur Jannat3Mohammad Omar Faruk4Jahirul Islam5Saydul Akbar Murad6Apurba Adhikary7Anupam Kumar Bairagi8Department of Information and Communication Engineering, Noakhali Science and Technology UniversityDepartment of Information and Communication Engineering, Noakhali Science and Technology UniversityDepartment of Information and Communication Engineering, Noakhali Science and Technology UniversityDepartment of Statistics, Noakhali Science and Technology UniversityDepartment of Statistics, Noakhali Science and Technology UniversityDepartment of Computer Science and Engineering, New Mexico Institute of Mining and TechnologySchool of Computing Sciences and Engineering, University of Southern MississippiDepartment of Information and Communication Engineering, Noakhali Science and Technology UniversityComputing Sciences and Engineering Discipline, Khulna UniversityAbstract The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encompass densely populated areas, adherence to mask-wearing protocols, inadequate awareness levels, and various behavioral and social practices. Despite the extensive research surrounding COVID-19 detection, an unfortunate dearth of studies has emerged to meticulously evaluate the intricate interplay between socio-demographic and behavioral factors and the likelihood of COVID-19 infection. Thus, a comprehensive online-based cross-sectional survey was methodically orchestrated, amassing data from a substantial sample size of 500 respondents. The precisely designed survey questionnaire encompassed various variables encompassing socio-demographics, behaviors, and social factors. The Bivariate Pearson’s Chi-square association test was deftly employed to unravel the complex associations between the explanatory variables and COVID-19 infection. The feature importance approach was also introduced to discern the utmost critical features underpinning this infectious predicament. Four distinct Machine Learning (ML) algorithms, specifically Decision Tree, Random Forest, CatBoost, and XGBoost, were employed to accurately predict COVID-19 infection based on a comprehensive analysis of socio-demographic and behavioral factors. The performance of these models was rigorously assessed using a range of evaluation metrics, including accuracy, recall, precision, ROC-AUC score, and F1 score. Pearson’s Chi-square test revealed a statistically significant association between vaccination status and COVID-19 infection. The use of sanitizer and masks, the timing of infection, and the interval between the first and second vaccine doses were significantly correlated with the likelihood of contracting the COVID-19 virus. Among the ML models tested, the XGBoost classifier demonstrated the highest classification accuracy, achieving an impressive 97.6%. These findings provide valuable insights for individuals, communities, and policymakers to implement targeted strategies aimed at mitigating the impact of the COVID-19 pandemic.https://doi.org/10.1007/s44230-023-00049-9COVID-19Socio-demographicBehaviorPearson Chi-squareMachine Learning |
| spellingShingle | K. M. Aslam Uddin Farida Siddiqi Prity Maisha Tasnim Sumiya Nur Jannat Mohammad Omar Faruk Jahirul Islam Saydul Akbar Murad Apurba Adhikary Anupam Kumar Bairagi Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection Human-Centric Intelligent Systems COVID-19 Socio-demographic Behavior Pearson Chi-square Machine Learning |
| title | Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection |
| title_full | Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection |
| title_fullStr | Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection |
| title_full_unstemmed | Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection |
| title_short | Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection |
| title_sort | machine learning based screening solution for covid 19 cases investigation socio demographic and behavioral factors analysis and covid 19 detection |
| topic | COVID-19 Socio-demographic Behavior Pearson Chi-square Machine Learning |
| url | https://doi.org/10.1007/s44230-023-00049-9 |
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