1. Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection.
- Author
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Amani Yahyaoui, Rasheed, Jawad, Alsubai, Shtwai, Shubair, Raed M., Alqahtani, Abdullah, Isler, Buket, and Haider, Rana Zeeshan
- Subjects
MACHINE learning ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,COVID-19 ,DEEP learning ,COVID-19 pandemic - Abstract
The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: Knearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long-term memory (LSTM) as DL methods. These approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo, Brazil. This data consists of 5644 laboratory results from different patients, with 10% being Covid-19 positive cases. The dataset includes 18 attributes that characterizeCOVID-19. We used accuracy, f1-score, recall and precision to evaluate the different developed systems. The obtained results confirmed these approaches' effectiveness in identifying COVID-19, However, ML-based classifiers couldn't performup to the standards achieved byDL-basedmodels. Among all, NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56% and 85% accuracies, respectively. Besides these, DL models attained better performance as CNN, DNN and LSTM secured more than 90% accuracies. The LTSM outperformed all by achieving an accuracy of 96.78% and an F1-score of 96.58%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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