Machine Learning Classifiers and Data Synthesis Techniques to Tackle with Highly Imbalanced COVID-19 Data

Machine Learning Classifiers and Data Synthesis Techniques to Tackle with Highly Imbalanced COVID-19 Data

The COVID-19 pandemic has highlighted the urgent need for rapid and accurate diagnostic methods. In this study, we evaluate three machine learning models—Random Forest (RF), Logistic Regression (LR) and Decision Tree (DT)—for detecting COVID-19 trained on preprocessed imbalanced datasets with 5086 n...

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Bibliographic Details
Main Authors: Avaz Naghipour, Mohammad Reza Abbaszadeh Bavil Soflaei, mostafa ghader-zefrehei
Format: Article
Language:English
Published: Ferdowsi University of Mashhad 2024-12-01
Series:Computer and Knowledge Engineering
Subjects:
covid-19 detection
machine learning
ctgan
tvae
class imbalance
Online Access:https://cke.um.ac.ir/article_45898_b3c8e1d9ecf92ea8a3734a1aab782226.pdf
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