TY  - EJOU
AU  - Izhar, Muhammad 
AU  - Parwez, Khadija 
AU  - Iftikhar, Saman 
AU  - Ahmad, Adeel 
AU  - Bawazeer, Shaikhan 
AU  - Abdullah, Saima 

TI  - Cyber-Integrated Predictive Framework for Gynecological Cancer Detection: Leveraging Machine Learning on Numerical Data amidst Cyber-Physical Attack Resilience
T2  - Journal on Artificial Intelligence

PY  - 2025
VL  - 7
IS  - 1
SN  - 2579-003X

AB  - The growing intersection of gynecological cancer diagnosis and cybersecurity vulnerabilities in healthcare necessitates integrated solutions that address both diagnostic accuracy and data protection. With increasing reliance on IoT-enabled medical devices, digital twins, and interconnected healthcare systems, the risk of cyber-physical attacks has escalated significantly. Traditional approaches to machine learning (ML)–based diagnosis often lack real-time threat adaptability and privacy preservation, while cybersecurity frameworks fall short in maintaining clinical relevance. This study introduces HealthSecureNet, a novel Cyber-Integrated Predictive Framework designed to detect gynecological cancer and mitigate cybersecurity threats in real time simultaneously. The proposed model employs a three-tier ML architecture incorporating Gradient Boosting and Support Vector Machines (SVMs) for accurate cancer classification, combined with an adaptive anomaly detection layer leveraging Mahalanobis Distance and severity scoring for threat prioritization. To enhance resilience, the framework integrates Zero Trust principles and Federated Learning (FL), enabling secure, decentralized model training while preserving patient privacy and meeting compliance with HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulations). Experimental evaluation using a real-world healthcare cybersecurity dataset demonstrated high accuracy (95.2%), precision (94.3%), recall (91.7%), and AUC-ROC (Area Under the Curve-Receiver Operating Characteristic) (0.94), with a low false positive rate (3.6%). HealthSecureNet outperforms traditional models such as SVM, Random Forest (RF), and k-NN (k-Nearest Neighbor) in both anomaly detection and severity classification accuracy. Its adaptive thresholding and response prioritization mechanisms make it suitable for dynamic healthcare environments, enabling early cancer detection and proactive cyber threat mitigation without compromising performance or regulatory standards. This research contributes a robust, dual-purpose solution that enhances both clinical diagnostics and cybersecurity, setting a precedent for future AI (Artificial Intelligence)-driven healthcare systems.
KW  - Gynecological cancer detection; machine learning (ML); cyber-physical security; predictive healthcare model; anomaly detection

DO  - 10.32604/jai.2025.062479