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March 4, 2026Health Informatics Journal1 citationsOpen Access

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

MAMohammad AlhefdiKing Khalid University YAYosuef AlotaibiKing Khalid University PRPaul RodriguesKing Khalid University

Key Points

The study aims to develop and evaluate a novel Random Coupled Bootstrapped Ensemble Classifier for detecting intrusions in healthcare systems.
Developed a Random Coupled Bootstrapped Ensemble Classifier (RCBEC) for intrusion detection.
Applied Data Preprocessing techniques including Decimal Score Max Normalization.
Used K-Best Kernel Discriminant Analysis (K-BKDA) for effective feature extraction.
Optimized feature identification with Hunter Canis Algorithm (HCA).
Implemented the model in a Python-based ECU-IoHT environment.
Achieved 99.6% accuracy and F1-score in cyberattack detection.
Outperformed existing intrusion detection methods.
Demonstrated superior generalization across diverse datasets.

Abstract

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.

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