Study by Himanshu Gupta · Om Prakash Verma. The COVID-19 pandemic has badly affected people of all ages globally. Therefore, its vaccine has been developed and made available for public use in unprecedented times. However, because of various levels of hesitancy, it did not have general acceptance. The main objective of this work is to identify the risk associated with the COVID-19 vaccines by developing a prognosis tool that will help in enhancing its acceptability and therefore, reducing the lethality of SARS-CoV-2.

1. Adverse effects of COVID-19
vaccination: Machine
learning and statistical
approach
Rama Irsheidat

2. 01 02 03
04 05
TABLE OF CONTENTS
SECTION
Introduction
SECTION
Goal
SECTION
Methodology
SECTION
Conclusion
SECTION
Critique

3. INTRODUCTION
01.

4. Vaccination
Is a well-accepted reliable approach to
preventing diseases. It has proven to be one
of the most effective strategies to control
pandemics, such as the SARS-CoV-2 outbreak.
All vaccines result in at least a small number
of patients that demonstrate some kind of
post-vaccination side effects. Although
vaccines are life-saving medications, they can
sometimes result in an after-effect,
sometimes even resulting in severe
symptoms, although with a low probability.

5. GOAL
02.

6. ● Identify and analyze the most probable causes in
the individuals' medical history that resulted in
adverse reactions to vaccination.
● Identify the key symptoms that indicate the cause
or causes of the adverse reactions.
● Developing the ML models for the prediction and
classification of individuals most susceptible to
the adverse effects of vaccination and therefore,
may require high medical attention.

7. METHODOLOGY
03.

8. LET’S START
Dataset
&
Feature extraction
STEP 1
Statistical methods
&
ML models
STEP 2
Technology
&
Evaluation metrics
STEP 3
Discussion
STEP 4
Result
STEP 5

9. ● Vaccine Adverse Event Reporting System (VAERS) dataset
○ General data (Such as: Age, Sex, Current illness, Medical
history, Allergic history, etc.)
○ Vaccination status
○ Post-vaccination symptoms
● More than 354 thousand samples
● Individuals who have been vaccinated between 1st
January 2021 to 11th June 2021 and also reported adverse
reactions.
Dataset

10. ● Convert features in textual format into attributes by
employing the String matching technique.
Feature extraction

11. ● Diseases with greater than 500 counts in medical history
have been considered as attributes and the rest
neglected.
● Based on the frequency of symptoms, a total of 49
symptoms have been used here for the analysis.
Feature extraction

12. 85
different features for over 354 thousand samples.

14. ● Chi-square ( χ2 ) Test
● The test of independence analyses the association
between various attributes of the dataset and the
outcome (target).
● Target variables (Outcome)
○ Died
○ Hospitalized
○ COVID-19 positive
Statistical methods

15. ● Logistic Regression (LR)
● Random Forest (RF)
● Naive Bayes (NB)
● Light Grading Boosting Machine (LGBM)
● Multilayer feed-forward Perceptron (MLP)
ML models

16. Logistic Regression (LR)
● Logistic regression is a predictive
analysis.
● Used to describe data and to explain the
relationship between one dependent
binary variable and one or more nominal,
ordinal, interval or ratio-level
independent variables.
● The Line is fit to the data using maximum
likelihood.

17. Light Gradient Boosting
Machine (LGBM)
● LightGBM is a gradient boosting algorithm that employs a tree-
based learning framework. As compared to other tree-based
frameworks, it grew trees vertically (leaf-wise) whereas, others
horizontally (level-wise).

18. Multilayer feed-forward
Perceptron (MLP)
● The MLP is a type of neural network which may
have one input layer, multiple hidden layers, and
one output layer.
 All these layers contain several artificial
neurons that have been connected with each
other in a unidirectional manner by mesh
arrangements.
● It is a mathematical model which aims to mimic
the functioning of human brains.

19. ● Python
● Keras (Keras is an open-source software library that provides a
Python interface for artificial neural networks.)
Technology

20. Evaluation metrics

21. ● The investigation has been carried out in three
scenarios:
○ Based on medical history only
○ Based on the reaction of vaccination only
○ Based on both medical history and adverse reaction
● The important contributing features have been identified
by both statistical analysis and developed ML models
(LR, RF, and LGBM) in all scenarios.
● All the developed ML models have been used to predict
the important key outcomes of interest (Death,
Hospitalization, and COVID-19 positive).
Discussion

23. Result

24. Died Hospitalized
COVID-19 positive

26. Result

27. Died Hospitalized
COVID-19 positive

29. Result

30. Died Hospitalized
COVID-19 positive

31. CONCLUSION
04.

32. ● The people in the age group of 50–70 have been found as most susceptible to the SARS-CoV-2.
● The male population has been identified as more vulnerable than to female population.
● The population with a history of life-threatening diseases should be vaccinated in close
observation.
● The most common post-vaccination symptoms have been identified. Most of these major
symptoms have been found normal and do not indicate towards any sign of serious and
immediate concern.
Conclusion

33. CRITIQUE
05.

34. ● Strengths :
○ This has been the very first study that analyses the impact of COVID-19 vaccines
employing more than 354 thousand samples.
● Weaknesses:
○ In dataset there was twice the number of female participants compared to male
participants and almost half of them were recorded as regularly taking other
medications.
● Limitations:
○ The study is conducted on USA population.
Critique

35. CREDITS: This presentation template was created by
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THANKS!