Journal of Applied Data Sciences
Vol 2, No 3: SEPTEMBER 2021

Data Mining Predicts the Need for Immunization Vaccines Using the Naive Bayes Method

Widyanto, R Arri (Unknown)
Avizenna, Meidar Hadi (Unknown)
Prabowo, Nugroho Agung (Unknown)
Alfata, Kemal (Unknown)
Ismanto, Agus (Unknown)



Article Info

Publish Date
29 Sep 2021

Abstract

In December 2019, SARS-CoV-2 caused the coronavirus disease (COVID-19) to spread to all countries, infecting thousands of people and causing death. COVID-19 causes mild illness in most cases, although it can make some people seriously ill. Therefore, vaccines are in various phases of clinical progress, and some of them have been approved for national use. The current state of affairs reveals that there is a critical need for a quick and timely solution to the need for a Covid-19 vaccine. Non-clinical methods such as data mining and machine learning techniques can help to do this. This study will focus on US COVID-19 Vaccination Advances using Machine learning classification algorithms and Using Geospatial analysis to visualize the results. The paper's findings indicate which algorithm is better for a given data set. Naive Bayes algorithm is used to run tests on real world data, and is used to analyze and draw conclusions. Period of Accuracy and performance, and it was found that Naive Bayes is very superior in terms of time and accuracy.

Copyrights © 2021






Journal Info

Abbrev

JADS

Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes ...