ItemConference Paper

Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions

Title
Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions
Creator
Balamurugan, Rahul; Sekar, Kency Taniya Antony; Nandalal, V.; Gracia, Carlos Gershenson
Description
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.
Source
2025 IEEE 1st International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability, ICINVENTS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Complex Systems; Diabetes Mellitus; Healthcare Inequity; Machine Learning; Mutual Information Network; Non-Linear Interactions
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155662-4;
Format
online
Language
English
Type
Conference paper

Citation

Balamurugan, Rahul; Sekar, Kency Taniya Antony; Nandalal, V.; Gracia, Carlos Gershenson, “Charting the Complexity of Diabetes Risk using Network-based Exploration of Nonlinear Interactions,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/26038.

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