Department of Computing and Technology, Iqra University, Islamabad, Pakistan.
Iqra University, Islamabad, Pakistan.
PLoS One. 2023 Oct 11;18(10):e0292587. doi: 10.1371/journal.pone.0292587. eCollection 2023.
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.
冠状病毒病（COVID-19）已在全球范围内引发大流行，继续对全球人类生命造成严重影响。其症状与肺炎相似，传播迅速，需要创新策略进行早期检测和管理。针对这一危机，数据科学和机器学习（ML）为解决包括 COVID-19 在内的复杂问题提供了关键解决方案。使用胸部 X 光片检测疾病是一种具有成本效益的方法，胸部 X 光是一种常见的初始检测方法。虽然现有的技术对于使用 X 射线检测 COVID-19 很有用，但在效率方面，特别是在培训和执行时间方面，还需要进一步改进。本文介绍了一种先进的架构，该架构利用集成学习技术从胸部 X 射线图像中检测 COVID-19。该模型使用并行和分布式框架，将集成学习与大数据分析相结合，以促进并行处理。该方法旨在提高执行和训练时间，确保更有效的检测过程。通过对预测值和实际值的综合分析验证了模型的有效性，并对其准确性、精度、召回率和 F 值进行了仔细评估，并与最先进的模型进行了比较。本文的工作不仅为抗击 COVID-19 做出了贡献，而且展示了集成学习技术在医疗保健中的更广泛适用性和潜力。
