Document Type
Article
Publication Date
2026
Journal / Book Title
Journal of Organizational and End User Computing
Abstract
Diabetes mellitus is a progressive metabolic disorder requiring timely identification to prevent severe complications and reduce healthcare burdens. This paper proposes an optimization-driven deep learning framework for accurate diabetes prediction, integrating Long Short-Term Memory selection and hyperparameter tuning. The framework is conceptualized as a decision-support system embedded within electronic health records and clinical workflows, supporting physicians, nurses, case workflow integration, governance, and privacy-are addressed to ensure alignment with real-world organizational contexts. The results demonstrate both technical feasibility and practical relevance, linking predictive analytics capability with human-AI collaboration, adoption factors, and operational decision-making in healthcare organizations.
DOI
10.4018/JOEUC.411868
MSU Digital Commons Citation
El Bouhissi, H; Xu, SB; and Wang, John H, "An Optimization-Driven Approach for Accurate Prediction of Diabetes Mellitus" (2026). Department of Information Management and Business Analytics Faculty Scholarship and Creative Works. 220.
https://digitalcommons.montclair.edu/infomgmt-busanalytics-facpubs/220
Rights
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creative-commons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited
Published Citation
El Bouhissi, H., Xu, S., & Wang, J. (2026). An Optimization-Driven Approach for Accurate Prediction of Diabetes Mellitus. Journal of Organizational and End User Computing (JOEUC), 38(1), 1-25. https://doi.org/10.4018/JOEUC.411868