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 Chintala, Sridhar; Janapati, RaviChander; Bhardwaj, Payal; Gude, Srihari Title A name given to the resource NAFLD Detection Using Natural Gradient Boosting: A Probabilistic Ensemble Approach for Improved Accuracy and Calibration 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 Lecture Notes in Electrical Engineering;Volume;1528 LNEE;pp.252-260 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-5831-5_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-5831-5_27</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031483567?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031483567?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 Chintala S., School of CS&amp;AI, SR University, Telangana, Warangal, India; Janapati R., School of CS&amp;AI, SR University, Telangana, Warangal, India; Bhardwaj P., Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Off-Campus Deoghar, Jharkhand, Deoghar, 814142, India; Gude S., Department of Electrical and Electronics Engineering, Christ (Deemed to be University), Karnataka, Bengaluru, India Description An account of the resource A growing global health concern, non-alcoholic fatty liver disease (NAFLD) must be accurately and promptly detected to avoid serious complications. This study suggests a model based on Natural Gradient Boosting (NGBoost) for accurate clinical feature-based NAFLD prediction. In contrast to traditional gradient boosting algorithms, NGBoost uses natural gradients to estimate the entire conditional probability distribution of outcomes, which enhances uncertainty quantification and calibration. Using a publicly accessible Kaggle dataset, the models performance was compared to KNN, SVM, and Decision Tree classifiers. According to experimental results, NGBoost outperformed conventional classifiers in terms of precision, recall, and F1 score, achieving the highest accuracy of 92.8%. Excellent discriminative ability was indicated by the ROC curve, and strong generalization ability with minimal overfitting was confirmed by the trainingvalidation loss analysis. These findings demonstrate how NGBoost may be used to support clinical decisions, allowing for earlier detection and treatment. Subsequent research endeavors will investigate the validation of the model on more extensive real-world datasets and broaden its relevance to additional liver-related conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Decision Tree; KNN; Machine learning; NGBoost; Non-alcoholic fatty liver disease (NAFLD) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981955830-8; 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