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

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 ۹۹.۶۶% accuracy, F۱ score of ۹۹.۷۵%, and AUC of ۹۹.۹۶%. 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.

کلیدواژه ها:

Heart Disease Detection ، IoT–Fog–Cloud Computing ، CGAN-based Data Balancing Hybrid Feature Selection ، XGBoost ، TabTransformer