Optimized XGBoost-Based Model for Accurate Detection and Classification of COVID-19 Pneumonia

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ID: 312942
2024
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Abstract
The accurate diagnosis of COVID-19 pneumonia is a critical global health challenge, particularly for vulnerable populations. Existing diagnostic methods often lack precision due to limited algorithm sophistication and insufficient dataset validation. This study addresses these issues by introducing a customized XGBoost algorithm for classifying COVID-19 pneumonia. The methodology follows a four-phase approach: (1) data acquisition from a comprehensive GitHub dataset, (2) data preprocessing with augmentation and normalization, (3) model training using XGBoost, and (4) evaluation against existing models. The model achieves an average accuracy of 87.35%, demonstrating superior performance in accuracy and diagnostic precision compared to current methods. The findings of this research provides a systematic framework for improving pneumonia classification and sets the stage for future AI-driven healthcare advancements in respiratory diseases.
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imported_1777057963_69ebc0abbaf74 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fazal Malik, Muhammad Suliman, Muhammad Qasim Khan, Noor Rahman, Mohammad Khan
Journal Journal of Computing & Biomedical Informatics
Year 2024
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DOI not found
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https://jcbi.org/index.php/Main/article/view/723
Keywords Keywords not found

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