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 Conference Paper Faculty Publications- Conference Papers 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 Vishal, B.G.; George, Jossy; Chanti, S. Title A name given to the resource A Novel Machine Learning Approach for Tuberculosis Detection using Volatile Organic Compounds Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 4th International Conference on Intelligent Computing, Information and Control Systems, ICOIICS 2025;pp.1612-1618 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICOIICS67115.2025.11390373" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOIICS67115.2025.11390373</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036734426?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036734426?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 Vishal B.G., CHRIST (Deemed to be University), Bangalore, India; George J., CHRIST (Deemed to be University), Bangalore, India; Chanti S., CHRIST (Deemed to be University), Bangalore, India Description An account of the resource For world health, better TB diagnosis is still absolutely necessary. Using VOC Atlas, this study assesses a few machine learning techniques for categorizing breath samples depending on volatile organic compound (VOC) profiles. We created a machine learning pipeline and tried out four different models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), and a 1D-Convolutional Neural Network (1D-CNN). There were 1,500 patient profiles in the dataset spanning three groups: healthy people, drug-sensitive TB cases, and multidrug-resistant TB cases. Using VOC biomarker patterns found in VOC Atlas and prior TB research, these profiles were developed. While XGBoost stood out by reaching 100% accuracy, our studies revealed that most models performed rather well. This implies that gradient boosting-based ensemble models can adequately grasp the complex patterns found in breath data. One major caveat is that we have not tested these models on real clinical breath samples to validate them. Testing these models with actual patient samples in clinical settings would be the next reasonable step. All told, this research provides a strong basis for creating non-invasive ways to detect illnesses. 2025 IEEE. Subject The topic of the resource 1D-CNN; Breath Biopsy; Classification; Machine Learning; MDR-TB; Non-invasive Diagnosis; Random Forest; Tuberculosis (TB); Volatile Organic Compounds (VOCs); XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159897-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online