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 Balamurugan, Rahul; Sekar, Kency Taniya Antony; Nandalal, V.; Gracia, Carlos Gershenson Title A name given to the resource Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions 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 2025 IEEE 1st International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability, ICINVENTS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICINVENTS64613.2025.11401397" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICINVENTS64613.2025.11401397</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036965109?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036965109?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 Balamurugan R., School of Systems Science and Industrial Engineering, Binghamton University, New York, United States, School of Science, CHRIST (Deemed to be University), Lavasa, Pune, India; Sekar K.T.A., School of Systems Science and Industrial Engineering, Binghamton University, New York, United States; Nandalal V., Sri Krishna College of Engineering and Technology, Coimbatore, India; Gracia C.G., School of Systems Science and Industrial Engineering, Binghamton University, New York, United States Description An account of the resource Diabetes mellitus is a global health challenge shaped by complex clinical, demographic, and socioenvironmental factors. Traditional linear models often overlook the non-linear dependencies that drive diabetes risk. This study adopts a systems-thinking approach by integrating mutual information (MI)-based network modeling with machine learning to improve prediction, interpretability, and fairness. Using a nationally representative CDC dataset, we build a weighted undirected network where variables are nodes connected by MI-derived edges. Centrality analysis identifies age, HbA1c, and BMI as key hubs. Community analysis reveals clinical, demographic, and racial modules, reflecting the multidimensional nature of diabetes risk. These network insights inform feature selection for training logistic regression, random forest, and XGBoost models. XGBoost achieves the highest accuracy (95.3%) and AUC (0.939), while logistic regression offers the best calibration (Brier score = 0.045), enhancing clinical usability. Subgroup analysis shows stable predictions across racial groups, supporting fairness. This integrated framework uncovers latent, non-linear associations and offers a robust, interpretable, and equitable tool for precision diabetes risk modeling. 2025 IEEE. Subject The topic of the resource Complex Systems; Diabetes Mellitus; Healthcare Inequity; Machine Learning; Mutual Information Network; Non-Linear Interactions Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155662-4; 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