Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study

Amir Ahmad, Ourooj Safi, Sharaf Malebary, Sami Alesawi, Entisar Alkayal and Atila Bueno

Complexity, 2021, vol. 2021, 1-8

Abstract: The coronavirus disease 2019 (Covid-19) pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms were applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5550344

DOI: 10.1155/2021/5550344

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