AI based predictive acceptability model for effective vaccine delivery in healthcare systems
Abstract Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of ne...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-76891-z |
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| author | Muhammad Shuaib Qureshi Muhammad Bilal Qureshi Urooj Iqrar Ali Raza Yazeed Yasin Ghadi Nisreen Innab Masoud Alajmi Ayman Qahmash |
| author_facet | Muhammad Shuaib Qureshi Muhammad Bilal Qureshi Urooj Iqrar Ali Raza Yazeed Yasin Ghadi Nisreen Innab Masoud Alajmi Ayman Qahmash |
| author_sort | Muhammad Shuaib Qureshi |
| collection | DOAJ |
| description | Abstract Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents’ occupation, and mother’s education have an association with vaccine acceptability. |
| format | Article |
| id | doaj-art-69bf8eee96f543a58b420003594a2227 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-69bf8eee96f543a58b420003594a22272025-02-02T12:25:32ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-76891-zAI based predictive acceptability model for effective vaccine delivery in healthcare systemsMuhammad Shuaib Qureshi0Muhammad Bilal Qureshi1Urooj Iqrar2Ali Raza3Yazeed Yasin Ghadi4Nisreen Innab5Masoud Alajmi6Ayman Qahmash7School of Computing Sciences, Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyDepartment of Computer Science & IT, University of Lakki MarwatDepartment of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyDepartment of Computer Science, MY UniversityDepartment of Computer Science, Al Ain UniversityDepartment of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa UniversityDepartment of Computer Engineering, College of Computers and Information Technology, Taif UniversityDepartment of Informatics and computer systems, College of Computer Science, King Khalid UniversityAbstract Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents’ occupation, and mother’s education have an association with vaccine acceptability.https://doi.org/10.1038/s41598-024-76891-zAssociation Rule MiningChildhood immunizationFeature importanceMachine learningVaccinationVaccine Acceptance |
| spellingShingle | Muhammad Shuaib Qureshi Muhammad Bilal Qureshi Urooj Iqrar Ali Raza Yazeed Yasin Ghadi Nisreen Innab Masoud Alajmi Ayman Qahmash AI based predictive acceptability model for effective vaccine delivery in healthcare systems Scientific Reports Association Rule Mining Childhood immunization Feature importance Machine learning Vaccination Vaccine Acceptance |
| title | AI based predictive acceptability model for effective vaccine delivery in healthcare systems |
| title_full | AI based predictive acceptability model for effective vaccine delivery in healthcare systems |
| title_fullStr | AI based predictive acceptability model for effective vaccine delivery in healthcare systems |
| title_full_unstemmed | AI based predictive acceptability model for effective vaccine delivery in healthcare systems |
| title_short | AI based predictive acceptability model for effective vaccine delivery in healthcare systems |
| title_sort | ai based predictive acceptability model for effective vaccine delivery in healthcare systems |
| topic | Association Rule Mining Childhood immunization Feature importance Machine learning Vaccination Vaccine Acceptance |
| url | https://doi.org/10.1038/s41598-024-76891-z |
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