Journal of AI and Data Mining

Quarterly

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Journal Metrics

A Dual-Path Learning Framework for Real-Time Heart Disease Prediction in IoT-Fog-Cloud Environments Using CGAN and Hybrid Feature Selection

Document Type : Research Note

Authors

1 Department of Electrical Engineering, University of Birjand, Birjand, Iran.

2 Department of Computer Science, Birjand University of Technology, Birjand, Iran.

10.22044/jadm.2026.17225.2858

Abstract

Accurate and timely detection of heart disease is a fundamental challenge in clinical diagnostics, exacerbated by the inherent complexities of real-world healthcare datasets, such as class imbalance, high dimensionality and the presence of outliers. To address these issues, we propose a dual-path learning framework specifically designed for real-time heart disease detection within IoT–Fog–Cloud environments. The framework integrates advanced preprocessing techniques, including Conditional Generative Adversarial Networks (CGANs) for data balancing and a hybrid feature selection pipeline, thereby addressing data imbalance and dimensionality reduction. It also proposes a dual-path diagnostic system that uses a lightweight XGBoost classifier in the fog layer optimized for low latency and fast local inference, and an improved TabTransformer-based model in the cloud layer, which is selectively used for ambiguous samples. Experimental evaluation on heart disease datasets demonstrates the superior performance of the framework, achieving 99.66% accuracy, F1 score of 99.75%, and AUC of 99.96%. Additionally, it improves overall inference latencies, mostly below one millisecond in the fog layer and low latency in cloud layer. This paper provides a scalable, interpretable, and privacy-preserving medical diagnosis framework in distributed healthcare systems.

Keywords

Main Subjects

References
[1] H. Ahmadi, G. Arji, L. Shahmoradi, R. Safdari, M. Nilashi, and M. Alizadeh, "The application of internet of things in healthcare: a systematic literature review and classification," Universal Access in the Information Society, vol. 18, no. 4, pp. 837–869, 2019.
 
[2] N. Taimoor and S. Rehman, "Reliable and resilient AI and IoT-based personalised healthcare services: A survey," IEEE Access, vol. 10, no. 2, pp. 535–563, 2021.
 
[3] S. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, "The internet of things for health care: a comprehensive survey," IEEE Access, vol. 3, no. 1, pp. 678–708, 2015.
 
[4] J. Heaney, J. Buick, M. U. Hadi, and N. Soin, "Internet of Things-based ECG and vitals healthcare monitoring system," Micromachines, vol. 13, no. 12, pp. 21-53, 2022.
 
[5] S. Rashid and A. Nemati, "Human-centered IoT-based health monitoring in the Healthcare 5.0 era: Literature descriptive analysis and future research guidelines," Discover Internet of Things, vol. 4, no. 1, pp. 26-37, 2024.
 
[6] A. Ilyas, M. N. Alatawi, Y. Hamid, S. Mahfooz, I.Zada, N. Gohar, and M. A. Shah, "Software architecture for pervasive critical health monitoring system using fog computing," Journal of Cloud Computing, vol. 11, no. 1, pp. 84-98, 2022.
 
[7] K. Kaliyaperumal, "Adaptive heuristic edge assisted fog computing design for healthcare data optimization," Journal of Cloud Computing, vol. 13, no. 1, pp. 1–18, 2024.
 
[8] N. Jeyaraman, S. Ramasubramanian, S. Yadav, S. Balaji, S. Muthu, and M. Jeyaraman, "Regulatory challenges and frameworks for fog computing in healthcare," Cureus, vol. 16, no. 8, pp.134-147,2024.
 
[9] Y. Y. Ghadi, S. F. A. Shah, T. Mazhar, T. Shahzad, K. Ouahada, and H. Hamam, "Enhancing patient healthcare with mobile edge computing and 5G: challenges and solutions for secure online health tools," Journal of Cloud Computing, vol. 13, no. 1, pp. 93-109, 2024.
 
[10] D. Navakauskas and M. Kazlauskas, "Fog computing in healthcare: systematic review," Informatica, vol. 34, no. 3, pp. 577–602, 2023.
 
[11] M. Butt, N. Tariq, M. Ashraf, H. S. Alsagri, S. A. Moqurrab, H. A. A. Alhakbani, and Y. A. Alduraywish, "A fog-based privacy-preserving federated learning system for smart healthcare applications," Electronics, vol. 12, no. 19, pp. 40-54, 2023.
 
[12] N. Kaur, A. Mittal, U. K. Lilhore, S. Simaiya, S. Dalal, K. Saleem, and E. S. Ghith, "Securing fog computing in healthcare with a zero-trust approach and blockchain," EURASIP Journal on Wireless Communications and Networking, vol. 2025, no. 1, pp. 5-18, 2025.
 
[13] H. Maneshti, M. Dadashi, and K. Rostami, "IoT-enabled low-cost fog computing system with online machine learning for accurate and low-latency heart monitoring in rural healthcare settings," arXiv preprint arXiv:2302.14131, 2023.
 
[14] W. Zhu, M. Goudarzi, and R. Buyya, "FLight: A lightweight federated learning framework in edge and fog computing," Software: Practice and Experience, vol. 54, no. 5, pp. 813–841, 2024.
 
[15] A. A. Gad-Elrab, A. S. Alsharkawy, M. E. Embabi, A. Sobhi, and F. A. Emara, "Adaptive multi-criteria-based load balancing technique for resource allocation in fog-cloud environments," arXiv preprint arXiv:2402.01326, 2024.
 
[16] R. Mohammadizand, R. Rastgoo,”SkeletonBased Sign Language Generation Using a Transformer-based Generative Model,”Journal of Artificial Intelligence and Data Mining, vol. 14, no.3, pp. 407-417, 2026.
 
[17] S. M. Rajagopal, M. Supriya, and R. Buyya, "Blockchain integrated federated learning in edge/fog/cloud systems for IoT-based healthcare applications: a survey," Federated Learning, vol. 34, no. 5,pp. 237–269, 2024.
 
[18] A. Kishor, C. Chakraborty, and W. Jeberson, "A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 7, pp. 7–17, 2021.
 
[19] Z. Nasiri Aghdam, A. Rezaee, A. M. Rahmani, and M. Hosseinzadeh, "Evolutionary-based hybrid classical-fuzzy heuristic protocol for real-time data availability of IoHT devices in cloud-fog computing," Cluster Computing, vol. 28, no. 14, pp. 1–39, 2025.
 
[20] S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, and R. Buyya, "HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments," Future Generation Computer Systems, vol. 104, no. 4, pp. 187–200, 2020.
 
[21] A. A. Nancy, D. Ravindran, D. R. Vincent, K. Srinivasan, and C.-Y. Chang, "Fog-based smart cardiovascular disease prediction system powered by modified gated recurrent unit," Diagnostics, vol. 13, no. 12, pp. 2071-2089, 2023.
 
[22] Jonas0402. "Heart Disease Dataset - Aggregated Clinical Records." GitHub Repository. https://github.com/jonas0402/Heart-Disease (accessed March 2024.
 
[23] A. Ogunpola, F. Saeed, S. Basurra, A. M. Albarrak, and S. N. Qasem, "Machine learning-based predictive models for detection of cardiovascular diseases," Diagnostics, vol. 14, no. 2, pp. 144-157, 2024.
 
[24] S. C. M. Sundararajan, G. Bharathi, U. Loganathan, and S. Vadivel, "Improved smart healthcare system of cloud-based IoT framework for the prediction of heart disease," Information Technology and Control, vol. 52, no. 2, pp. 529–540, 2023.
 
[25] H. J. Suleiman, I. R. A. Hamid, and O. R. Olaniran, "Smart Health Monitoring for Predicting Heart Disease using IoT-Fog-Cloud Computing Model," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 22565–22572, 2025.
 
[26] E. M. Priya and K. S. Krishnan, "LIFE‐CARE: IoT–Cloud‐Enabled Smart Heart Disease Prediction System for Smart Healthcare Environment Using Deep Learning," International Journal of Distributed Sensor Networks, vol. 2025, no. 1, pp. 696-715, 2025.
 
[27] A. Pati, M. Parhi, M. Alnabhan, B. K. Pattanayak, A. K. Habboush, and M. K. Al Nawayseh, "An IoT-fog-cloud integrated framework for real-time remote cardiovascular disease diagnosis," in Informatics, 2023, vol. 10, no. 1, pp. 21-34, 2024.
 
[28] M. K. Ravindranathan and N. Rajagopalan, "Fog-Driven Heart Attack Prediction from Wearable Edge Devices," in 2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S), 2024, vol. 1: IEEE, pp. 1–5, 2024.
 
[29] A. O. Mulani, M. P. Sardey, K. Kinage, S. S. Salunkhe, T. Fegade, and P. G. Fegade, "ML-powered Internet of Medical Things (MLIOMT) structure for heart disease prediction," Journal of Pharmacology and Pharmacotherapeutics, vol. 16, no. 1, pp. 38–45, 2025.
 
[30] L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni, "Modeling tabular data using conditional gan," Advances in neural information processing systems, vol. 32 no. 4,pp.34-48, 2019.
 
[31] J. Engelmann and S. Lessmann, "Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning," arXiv preprint arXiv:2008.09202, 2020.
 
[32] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, no. 4,,pp. 321–357, 2002.
 
[33] A. Marengo, A. Pagano, and V. Santamato, "An efficient cardiovascular disease prediction model through AI-driven IoT technology," Computers in Biology and Medicine, vol. 183 no. 4,, pp. 109-123, 2024.
 
Journal of AI and Data Mining
Volume 14, Issue 4
October 2026
Pages 543-557
Files
Share
How to cite
Statistics