###### Abstract  
The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using [PERSON]'s Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities. Internet of Medical Things (IoMT); patient privacy; security; authentication; clustering; encryption; routing +
Footnote †: journal: _Sensors_  
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considered to reduce the usage of energy [5]. Still, the energy constraint is a significant issue in the IIoT [6]. Another issue is that most of the cluster-based works are executed in homogeneous environments and residual energy or distance parameters are used for cluster formation [7]. To overcome this issue, heterogeneous sensor-based clustering has been a focus of research [8]. In addition, intelligent routing is a noteworthy phenomenon to improve the quality of service (QoS) during interoperable data communication [9]. Managing the QoS to improve network performance and scalability as well as security solutions was recently addressed using blockchain technology [10]. Blockchain is a decentralized technology that addresses single-point failures [11; 12]. However, resources are required to communicate with other IIoT devices. In general, insufficient resources cause huge packet loss or drop [13; 14]. Artificial intelligence (AI) plays a vital role in recent IIoT networks, where data of large size are generated and collected from a variety of devices [15]. They are widely used in different smart applications, such as smart cities, healthcare, industry, and so on [16]. In this study, AI acts as an intelligent agent to learn the IIoT environment and collect data from real-world scenarios [17]. AI requires training to classify data from multiple devices, but it often does not ensure reality [18]. Due to the environmental conditions, data collected with IIoT sensors require training for accurate classification. Support Vector Machine (SVM) is one such AI algorithm created for data classification [19]. Data mining techniques for patient health analysis have focused on the application of artificial intelligence techniques for analyzing healthcare data during the COVID-19 pandemic [20]. The effectiveness of deep learning models in analyzing such data is proved and potentially offers valuable insights for diagnostics and treatment [21].