Enhanced threat detection in health care systems with random coupled bootstrapped ensemble classifier

The protection of patient information in modern healthcare demands overcoming major challenges intensified by the integration of Internet of Things (IoT) technologies. The proposed Random Coupled Bootstrapped Ensemble Classifier (RCBEC) offers an advanced intrusion detection framework to enhance cyberattack detection in smart healthcare environments. The model optimizes both accuracy and feature selection to improve computational efficiency and precision. Data preprocessing employs Decimal Score Max Normalization for transformation, duplicate removal, and handling of missing values. Feature extraction through K-Best Kernel Discriminant Analysis (K-BKDA) and optimization via Hunter Canis Algorithm (HCA) ensure effective identification of attack-relevant features. Implemented in a Python-based ECU-IoHT environment, the RCBEC achieves 99.6% accuracy and F1-score, outperforming existing intrusion detection methods. The ensemble classifier combines rapid computational performance with robust threat identification capabilities, enhancing security in IoT-enabled healthcare systems. Comparative analysis demonstrates the system's superior generalization and adaptability across diverse datasets. Overall, the proposed RCBEC model establishes a resilient and intelligent mechanism for detecting and mitigating cybersecurity threats in healthcare networks. This work highlights how machine learning-driven intrusion detection significantly strengthens patient data protection, operational reliability, and trust in next-generation healthcare systems.