Evaluation Framework for Security of Internet of Health Things System : Integrating AHP-COBRA Method with Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy

Abstract Security systems are vital for safeguarding lives, property, and valuable information while ensuring community safety and peace of mind. In healthcare service delivery, the Internet of Things (IoT) has revolutionized operations by connecting billions of devices that automate tasks and generate massive data flows. This expansion has given rise to the Internet of Health Things (IoHT), also referred to as the Internet of Medical Things (IoMT), where smart medical devices are integrated into telehealth platforms. These systems allow healthcare professionals to access real-time patient data, thereby improving diagnosis, treatment, and overall patient care. However, the rapid proliferation of IoMT introduces significant security and privacy challenges due to the vast and complex ecosystem of connected devices. This paper presents a Systematic Literature Review (SLR) focused on addressing IoMT security and privacy issues using machine learning (ML) and advanced decision-making techniques. Specifically, the study leverages probabilistic uncertain linguistic term sets (PULTS) and q-rung orthopair fuzzy sets (q-ROFS) to handle ambiguity and uncertainty in IoMT security. To enhance decision-making reliability, the proposed framework integrates the Analytic Hierarchy Process (AHP) for criterion weighting with the Comprehensive Distance-Based Assessment (COBRA) for ranking alternatives. The combined PULq-ROF-AHP-COBRA model ensures stable priority assignment and balanced evaluation of security mechanisms.A numerical case study further demonstrates the practicality of this method, highlighting its ability to strengthen decision precision and improve security in healthcare IoMT environments.