Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chodagam, Yugandhar; Hiremath, Manjunatha Title A name given to the resource Pneumonia classification from chest X-rays using significant feature selection and machine learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived IAES International Journal of Artificial Intelligence;Volume;15;Issue;1;pp.592-603 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v15.i1.pp592-603" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i1.pp592-603</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031500747?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031500747?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chodagam Y., Department of Computer Science, Christ University, Bengaluru, India; Hiremath M., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource The chest X-ray images of normal lungs differ only subtly from those of lungs with pneumonia, making image-based diagnosis highly challenging. To address this issue, we developed a machine learning (ML)-based, lightweight, end-to-end Python package that processes chest X-ray images, implements robust feature selection methods, and classifies the images using various algorithms. While many studies have focused on improving classification accuracy using newer methods, few have addressed the interpretability of the extracted features or the growing computational demands of complex models. We used four publicly available datasets and extracted first-order, textural, and transform-based radiomic features to test our package. Features were selected using the Shapley additive explanations (SHAP) combined with recursive feature elimination (RFE) and stability selection algorithms. Our final solution contains a method that extracts a finite set of features identified by stability selection and feeds them as inputs into classical ML algorithms. Our model achieved 98% accuracy on the primary dataset, and 97%1, 96%2, and 94%2% accuracy on the other three datasets. Our approach is fast, self-contained, and requires only an ideal set of features, making it suitable for resource-constrained clinical environments. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/ Subject The topic of the resource Feature selection; Machine learning; Pneumonia; Radiomics; Wavelet features; X-ray Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online