<![CDATA[Pneumonia, a prevalent lung disease globally, poses significant challenges in accurate diagnosis despite its severity. This paper proposes a novel approach leveraging Support Vector Machine (SVM) classification and Gray-Level Co-occurrence Matrix (GLCM) analysis on chest X-ray images to aid in pneumonia diagnosis. By extracting pneumonia-indicative features from digital X-ray images using Gray-Level Co-occurrence Matrix (GLCM) and employing Support Vector Machine (SVM) for classification, the study aims to enhance pneumonia diagnosis effectiveness, particularly crucial in regions with limited healthcare resources. The proposed method focuses on identifying characteristic patterns indicative of pneumonia in chest X-ray images and distinguishing between normal and pneumonia-affected images based on GLCM-extracted features. Furthermore, the study evaluates the impact of hyperparameter tuning using grid search on the proposed diagnostic system's performance, including accuracy, sensitivity, and specificity. By achieving these objectives, the research aims to contribute significantly to the development of more accurate and effective diagnostic tools for pneumonia, especially in resource-constrained areas. Keywords: Gray-Level Co-occurrence Matrix (GLCM), Machine Learning, Pneumonia, Support Vector Machine, X-Ray]]>
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