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 Hazra, Chandrima; Jayan, Mini; Saleema, J.S. Title A name given to the resource Interpretable Breast Cancer Risk Stratification Using Statistical Feature Engineering on Thermal Images 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 Computer Science;Volume;16308 LNCS;pp.65-79 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-10990-3_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-10990-3_5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022979889?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022979889?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 Hazra C., Department of Statistics and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India; Jayan M., Department of Statistics and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India; Saleema J.S., Department of Statistics and Data Science, Christ (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource This research solves the black box problem of AI implementation in imaging by introducing a transparent, statistically grounded approach to breast cancer risk stratification via infrared thermography without compromising performance. Using the public DMR-IR dataset, statistical feature engineering was applied to training data by extracting first- and second-order statistical features. After ensuring non-normality with a Shapiro-Wilk test, feature significance was established with the Mann-Whitney U test. LASSO regularization selected the five most predictive features: mean, standard deviation, kurtosis, correlation, and energy. To counteract class imbalance, SMOTE was applied, and two machine learning modelslogistic regression and random forest (classifier)were trained on the balanced data and then evaluated on an unseen test dataset. Reporting an AUC of 0.98 over logistic regressions 0.96 reflects stringent statistical feature engineering and great generalization, creating a strong and interpretable model for breast cancer diagnosis in thermal images, and instills more clinical confidence in AI-based diagnostic systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Breast Cancer; Infrared Thermography; Interpretable AI; LASSO; Machine Learning; SMOTE; Statistical Feature Engineering Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 3029743; ISBN: 978-303210989-7; 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