Title: Adverse COVID-19 vaccination-related events in India: a cross-sectional study using machine learning to predict their severity

Authors: Hemangini Mohanty; Santilata Champati; Jyotiranjan Sahoo

Addresses: Centre for Data Science, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-751030, Odisha, India ' Department of Mathematics, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-751030, Odisha, India ' Department of Community Medicine, Institute of Medical Sciences and SUM Hospital, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-751030, Odisha, India

Abstract: The most effective method of preventing coronavirus illness is vaccination, despite its status as a global outbreak. In this study, India's COVID-19 vaccination's adverse consequences are evaluated, and build a model for predicting the severity of side effects. A cross-sectional study was conducted through an online survey among the Indian population who received at least a single dose of the COVID-19 vaccine. Then, data were statistically analysed, and machine learning tools were used to build a predictive model predicting the severity of side effects. A total of 3,222 participants' records were analysed for those participants receiving three different vaccines, i.e., Covaxin, Covishield, and SputnikV. Only 25% experienced mild-to-moderate side effects. The most common side effects recorded were fever/chills, headache, feeling pain at the injection site, tiredness, and fatigue. The people receiving the first dose (71.93%) had significant side effects compared to the second dose.

Keywords: SARS-CoV-2 vaccine; COVID-19 vaccine; post-vaccination symptoms; machine learning; predictive model.

DOI: 10.1504/IJRIS.2025.148714

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.5, pp.326 - 341

Received: 15 May 2023
Accepted: 04 Aug 2023
Published online: 21 Sep 2025 *

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