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All Journal The Eastasouth Journal of Information System and Computer Science
Borhan Uddin
University of Information Technology and Sciences (UITS), Dhaka 1212, Bangladesh
Computer Science & IT

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Explainable AI Framework for Precision Public Health in Metabolic Disorders: A Federated, Multi-Modal Predictive Modelling Approach for Early Detection and Intervention of Type 2 Diabetes Md Habibur Rahman; Md Nazibullah Khan; Sachin Das; Borhan Uddin
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i02.759

Abstract

One of the biggest public health problems of the twenty-first century is metabolic disorders, especially Type 2 diabetes (T2D). Morbidity, mortality, and medical expenses can be significantly decreased by early detection of at-risk people. However, nonlinear, multi-factorial, and high-dimensional interactions that influence the development of disease are not well captured by traditional risk-scoring methods. In order to predict and interpret the risk of type 2 diabetes and related metabolic disorders, this study creates an Explainable AI (XAI) framework for precision public health that combines multi-modal data, such as genomic profiles, lifestyle factors, socioeconomic determinants, and electronic health records (EHR). We create a federated, hybrid model that combines Random Forest classifiers, Deep Neural Networks (DNN), and Gradient Boosting Machines (LightGBM/XGBoost), building on federated and ensemble learning paradigms. Shapley Additive Explanations (SHAP) and counterfactual analysis are used to uncover personalized, actionable risk profiles in order to attain explainability. Harmonized multi-institutional datasets with over 200,000 records gathered from several U.S. health systems are used to train the model. The results show a calibrated Brier score of 0.12, sensitivity of 89%, specificity of 87%, and AUC of 0.93 ± 0.01. The socioeconomic deprivation index, polygenic risk score, BMI slope, and HbA1c trajectory are the main factors, according to SHAP study. Federated deployment protects data privacy while preserving performance. These results show that federated, explainable AI pipelines can facilitate population-based, privacy-preserving, andThe goal of precision public health is being advanced by large-scale early-warning systems for managing metabolic diseases.
Cognitive Cyber Defense: AI–MIS Integration through Big Data and Cloud Frameworks for Next-Generation Digital Resilience Md Delwar Hossain; Mohammad Somon Sikder; Md Salah Uddin; Rezwan Moin Ahsan; Borhan Uddin; Tawhid Hossen
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.764

Abstract

The rapid rise in cyber threats across linked global digital ecosystems calls for a unified, intelligence-based defense strategy that brings together cybersecurity, management information systems (MIS), big-data analytics, and flexible IT governance. This study builds on the work of Kaur et al. (2023), Hasan et al. (2023), Mahmud et al. (2023), and Das et al. (2023) to create a comprehensive framework that uses artificial intelligence (AI), cloud computing, and data-driven decision-making to make digital systems more resilient. The research formulates an integrated AI–MIS Cyber-Defense Framework via a meta-synthesis of present empirical studies, clarifying the interaction among machine-learning analytics, predictive threat intelligence, and adaptive governance feedback loops. These interdependencies together improve the accuracy of detection, the ability to understand the issue in context, and the ability of organizations to adjust in unstable cyber environments. Quantitative evaluation shows that the system works better than traditional control systems. The average detection area under the curve (AUC) is over 0.93, the precision–recall metrics are above 0.90, and the composite resilience index is 27 percent higher. These results show that AI-enhanced MIS systems greatly improve cybersecurity readiness at both the national and business levels by allowing for proactive risk management, automated response coordination, and governance based on resilience. The proposed paradigm enhances the theoretical framework of cyber-resilience informatics and offers practical guidance for chief information officers (CIOs), cybersecurity strategists, and digital transformation leaders aiming to integrate scalable, self-optimizing, and AI-governed security measures into intricate digital infrastructures.
Co-Authors Md Delwar Hossain Md Habibur Rahman Md Nazibullah Khan Md Salah Uddin Mohammad Somon Sikder Rezwan Moin Ahsan Sachin Das Tawhid Hossen