Comparison of Naive Bayes and PSO-Based Naive Bayes Algorithms for Prediction of Covid-19 Patient Recovery Data in Indonesia

Comparison of Naive Bayes and PSO-Based Naive Bayes Algorithms for Prediction of Covid-19 Patient Recovery Data in Indonesia

naive bayes algorithm; particle swarm optimization; covid-19 patients; data mining; classification

A brand-new illness known as COVID 19 was identified in 2019 but has yet to infect humans (World Health Organization,
2019). This group of viruses can infect mammals including humans as well as birds and cause sickness. People commonly
contract coronaviruses from the flu and other minor respiratory ailments, but they can also spread serious diseases like SARS,
MERS, and the deadly COVID-19. So that there are no more casualties, this number must be decreased. It is crucial to
understand the variables that can truly reduce the danger of death and gauge the propensity for recovery in Covid-19 patients.
Several techniques in data mining can be used to forecast patient recovery rates depending on various characteristics. This
study's criteria included gender, age, province, and status. The Naive Bayes (NB) and Pso-based Naive Bayes algorithms are
compared in this study using patient datasets to determine whether strategy is more accurate. The findings of this study reveal
that using the NB method has a 94.07% accuracy rate, a precision value of 14%, a recall value of 1%, and an AUC value of
0.613, according to the study's data. The accuracy rate of PSO-based Naive Bayes is 95.56%, the precision is 25%, the recall
is 1%, and the AUC is 0.540.

Alvina Felicia Watratan, Ema Utami, Anggit Dwi Hartanto

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

August 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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