Next-Generation Data Mining Techniques for Healthcare, IoT, and Cybersecurity: A Unified Framework

Abstract

The convergence of the Internet of Medical Things (IoMT), cloud computing, and pervasive healthcare systems has generated unparalleled volumes of real-time telemetry. However, this hyper-connectivity introduces critical vulnerabilities, making health networks premier targets for sophisticated cyber threats. Traditional data mining frameworks fail to balance real-time diagnostic mining with concurrent cryptographic security and threat detection. This paper proposes a unified, next-generation data mining framework that simultaneously extracts predictive clinical insights and neutralizes cybersecurity threats at the IoT edge. By integrating Federated Learning (FL), Generative Adversarial Networks (GANs) optimized via Adaptive Moment Estimation, and Homomorphic Encryption with Laplacian Differential Privacy, our system achieves high-accuracy diagnostic anomaly detection while maintaining a Zero-Trust cryptographic posture. Experimental simulations demonstrate a clinical pattern-extraction accuracy of 98.5% while keeping latency under critical thresholds for real-time IoMT deployment.