In the third phase, Recurrent Neural Network (RNN) is implemented for both each vaccine type and each target class. The patient’s status after vaccination dataset is grouped into three target classes (Death, Hospitalized, and Recovered).

In the first phase of the proposed model, the dataset has been pre-proceesed, while in the second phase, the Pigeon swarm optimization algorithm is used to optimally select the most promising features that affect the performance of the proposed model. The adverse reactions under study are the recovery condition, possibility to be hospitalized, and death status. PFIEZER, JANSSEN, and MODERNA) and the adverse reactions that may occur in vaccinated patients. In this paper, a Deep Learning (DL) model has been developed to identify the relationship between a certain type of COVID-19 vaccine (i.e.

The dataset used in this study is the Vaccine Adverse Event Reporting System (VAERS) dataset which was created as a corporation between the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to gather reported side effects that may be caused by PFIEZER, JANSSEN, and MODERNA vaccines. This paper aims to reduce COVID-19 vaccine hesitancy taking into consideration the patient’s medical history. On the other hand, excused confusion and hesitation have widely impacted public health. Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globalization of efforts and knowledge, a successful vaccine-based control strategy was efficiently designed and applied worldwide.

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