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<p>Nowadays, the Internet of Medical Things (IoMT) has received more attention in the healthcare management sector and healthcare services. The integration of healthcare devices and the Internet of Things (IoT) is called IoMT and makes it available for numerous healthcare activities to take place, such as real-time diagnosis, remote patient monitoring, and real-time medicine prescriptions, amongst other things. However, the security and privacy of the data, garnered by the IoMT devices, are primary problems while storing or transmitting it in the cloud. Implementing effective protocols and guaranteeing compatibility among the IoMT systems is significant to rectify this limitation. So, in this research, an innovative IoMT-based disease prediction model is presented. Initially, the requisite medical images are garnered from the standard datasets, and encryption is performed in the collected images by using the Hybrid Advanced Encryption standard with Rossler Hyperchaotic (HAERH). Further, the key needed for encryption is created using the Enhanced Pine cone Optimization (EPO). Subsequently, the encrypted images are decrypted to perform the disease prediction task using the Parameter Optimized ShuffleNet (PO-SNet) technique, where several parameters are tuned by the EPO to enhance the prediction performance. Finally, the designed PO-SNet model gives the disease-predicted outcome. Finally, the experiments are done for the developed work over traditional methods. The results show that the designed EPO-PO-SNet’s accuracy is 94.61% for the batch size value 48, while the NPCR of the designed HAERH is 94.97% for the block size value 25. Therefore, it is ensured that the designed system is effective and robust than the existing methods, improving the security of the IoMT.</p>

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A Novel Disease Prediction Process in IoMT Using Hybrid Encryption and Decryption Model with Optimized Deep Learning Strategy

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Garima Verma,
  • Shiva Prakash

摘要

Nowadays, the Internet of Medical Things (IoMT) has received more attention in the healthcare management sector and healthcare services. The integration of healthcare devices and the Internet of Things (IoT) is called IoMT and makes it available for numerous healthcare activities to take place, such as real-time diagnosis, remote patient monitoring, and real-time medicine prescriptions, amongst other things. However, the security and privacy of the data, garnered by the IoMT devices, are primary problems while storing or transmitting it in the cloud. Implementing effective protocols and guaranteeing compatibility among the IoMT systems is significant to rectify this limitation. So, in this research, an innovative IoMT-based disease prediction model is presented. Initially, the requisite medical images are garnered from the standard datasets, and encryption is performed in the collected images by using the Hybrid Advanced Encryption standard with Rossler Hyperchaotic (HAERH). Further, the key needed for encryption is created using the Enhanced Pine cone Optimization (EPO). Subsequently, the encrypted images are decrypted to perform the disease prediction task using the Parameter Optimized ShuffleNet (PO-SNet) technique, where several parameters are tuned by the EPO to enhance the prediction performance. Finally, the designed PO-SNet model gives the disease-predicted outcome. Finally, the experiments are done for the developed work over traditional methods. The results show that the designed EPO-PO-SNet’s accuracy is 94.61% for the batch size value 48, while the NPCR of the designed HAERH is 94.97% for the block size value 25. Therefore, it is ensured that the designed system is effective and robust than the existing methods, improving the security of the IoMT.