---

Prediction of Risks Associated with Mass Corona Vaccination

---

Related Links

1. Project page
2. Github repo
3. Azure DevOps Wiki Page
4. Department of computer engineering

Introduction

There is a trend among people not to get COVID-19 vaccinations. Because of the differing viewpoints of the various parties involved, society is skeptical of obtaining it. However, there is no research being done into the vaccination's side effects or the causes of illnesses and deaths. This project will look into the specific criteria or risks that come with vaccinations. So, this will help people to get an idea to check whether to take the vaccines or not.

Problem

Society is doubtful of getting COVID-19 vaccines due to spreading opinions, various myths, and fear of getting side effects. the above-mentioned problem. (what are their fears about getting the vaccine, Problem diagnosis: a small survey can be conducted, to get a clear idea about etc..)

Solution

To predict the risks associated with mass corona vaccination we are going to analyze different kinds of side effects (Fever,Itching,Coughing,Joint pain,Headache,Muscle pain,Swelling , Redness etc) with the following parameters,

• Age
• Gender
• Height and Weight
• Vaccine Type
• Blood Group
• Living Area

During the above analysis process first, we will try out different machine learning models, and then we will choose the best model by comparing the accuracy of each model. Our machine learning model will do the predictions on above mentioned side effects and finaly give a probability of being affected with a particular side effect. Finally, a web application that is combined with the best machine learning model will be developed. A database is maintaied at the backend at it will store the predictions along with the details provided by the user. Those data will be then used as another dataset.

Application features and description of the web application

1. Taking an information form from a person who is willing to take a vaccine and predict the risks (side effects) that can occur according to the developed model. That form contains the following information.

• age group
• gender
• blood group
• height and weight (BMI)
• vaccine type
• living area

2. Graphical representation of the analyzed data.

   

Use case diagram

Team Members

1. E/17/040 Chandrasena M.M.D [[email protected]]
2. E/17/356 Upekha H.P.S [[email protected]]
3. E/17/407 Wijesooriya H.D [[email protected]]