Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets

Nadiah Nadiah; Sopian Soim; Sholihin Sholihin

doi:10.24014/ijaidm.v5i1.17054

Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets

Nadiah Nadiah, Sopian Soim, Sholihin Sholihin

Abstract

The advancement of AI (Artificial Intelligence) technology has been widely implemented in numerous sectors of daily life. Machine Learning is one of the subfields of Artificial Intelligence. Using statistics, mathematics, and data mining, machine learning is developed so that machines may learn by assessing data without being reprogrammed. At this time the world is on alert for the spread of a popular virus, the corona virus. Coronaviruses are part of a family of viruses caused by diseases ranging from the flu. The disease caused by the coronavirus is known as Covid-19. Therefore, to help identify whether a somebody has coronavirus disease based on certain symptoms, a model is created that can classify people with the covid-19 virus using machine learning. The classification methods utilized in this study are decision trees and large-scale machine learning projects. The study employed Python 3.7 as its programming language and PyCharm as its Integrated Development Environment (IDE). Based on the results, the accuracy rate as expected after conducting various trials is 99%.

Keywords

Decision Tree Algorithm, Machine Learning, Covid-19 Virus, Classification, Public Datasets

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DOI: http://dx.doi.org/10.24014/ijaidm.v5i1.17054

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