Using Machine Learning for Identifying COVID-19
Keywords:
Machine Learning; Multilayer Perceptron; Random Forest; COVID-19; Supervised LearningAbstract
In late December 2019, an outbreak of the novel coronavirus, known as COVID-19, originated in Wuhan, China, ultimately evolving into a global pandemic. This study focuses on the application of two distinct machine learning approaches to predict COVID-19 presence in individuals. The dataset employed for analysis was obtained from clients who sought medical attention at Israelita Albert Einstein Hospital in São Paulo, Brazil. During their hospital visits, samples were collected for COVID-19 and additional laboratory tests. Specifically, we utilized supervised learning techniques, namely multilayer perceptron and random forest, to conduct our analysis. The performance of these models was evaluated using the Area under the ROC Curve (AUC), resulting in respective AUC values of 97% and 92.4% for multilayer perceptron and random forest methods.
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