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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 139 |
| Published: August 2026 |
| Authors: Shahriar Arefin Zummon, Somapika Das, Aushtmi Deb, Marjana Akter Juti |
10.5120/ijcaef834e7788bd
|
Shahriar Arefin Zummon, Somapika Das, Aushtmi Deb, Marjana Akter Juti . Effects of COVID-19 Vaccines among University Students in Bangladesh: A Survey-based Machine Learning Analysis. International Journal of Computer Applications. 187, 139 (August 2026), 1-12. DOI=10.5120/ijcaef834e7788bd
@article{ 10.5120/ijcaef834e7788bd,
author = { Shahriar Arefin Zummon,Somapika Das,Aushtmi Deb,Marjana Akter Juti },
title = { Effects of COVID-19 Vaccines among University Students in Bangladesh: A Survey-based Machine Learning Analysis },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 139 },
pages = { 1-12 },
doi = { 10.5120/ijcaef834e7788bd },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Shahriar Arefin Zummon
%A Somapika Das
%A Aushtmi Deb
%A Marjana Akter Juti
%T Effects of COVID-19 Vaccines among University Students in Bangladesh: A Survey-based Machine Learning Analysis%T
%J International Journal of Computer Applications
%V 187
%N 139
%P 1-12
%R 10.5120/ijcaef834e7788bd
%I Foundation of Computer Science (FCS), NY, USA
While young adults are generally considered resilient, the longterm side effects of COVID-19 vaccines in this demographic remain largely unexplored. This study investigates post-vaccination experiences among university students and faculty collected from three universities in Sylhet, Bangladesh through online and offline surveys between January and March 2025. After rigorous data cleaning, a finalized dataset of 591 respondents was obtained covering vaccine type, dosage, demographics, pre-existing health conditions, overseas travel history, and post-vaccination impacts (short-term, medium-term, and long-term). Descriptive, correlation, and predictive analyses were performed. Five supervised machine learning (ML) classifiers - Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), XGBoost, and K-Nearest Neighbors (KNN) - were evaluated alongside K-Means clustering (k = 4) with Principal Component Analysis (PCA) for patient risk profiling. Logistic Regression achieved the highest accuracy (70.79%). Random Forest feature importance identified side-effect severity (52.8%) and vaccine type (34.7%) as the dominant predictors. Respiratory conditions and allergies were the leading comorbidities associated with post-vaccination impacts. Findings provide actionable insights for policymakers and healthcare professionals refining vaccination protocols for young, active populations.