Ensemble learning for multi-class COVID-19 detection from big data

• Published in: PloS one Vol. 18; no. 10; p. e0292587
• Main Authors: Kaleem, Sarah, Sohail, Adnan, Tariq, Muhammad Usman, Babar, Muhammad, Qureshi, Basit
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 11.10.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Artificial intelligence; Big Data; Biology and life sciences; Chest; Computer and Information Sciences; Coronaviruses; COVID-19; Data science; Decision making; Ensemble learning; Epidemics; Machine learning; Medical imaging equipment; Medicine and Health Sciences; Methods; Multiprocessing; Neural networks; Outdoor air quality; Pandemics; Parallel processing; Research and Analysis Methods; Signs and symptoms; Training; Viral diseases; X-rays; Saudi Arabia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0292587

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model’s efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.