Internet of Things for sustaining a smart and secure healthcare system

The thyroid is a key endocrine gland in the human body that regulates several bodily processes, including protein synthesis, energy consumption, and the body's reaction to other hormones. Segmentation and volume regeneration of the thyroid is particularly important for identifying thyroid-related diseases since the majority of these problems result in a change in the thyroid's shape and scale over time. There is an urgent need for research on the disease's origins and spread. The Internet of Things, cloud computing, and artificial intelligence all provide real-time processing for a variety of applications in the healthcare sector. In healthcare and biomedicine applications, machine learning algorithms are increasingly being utilized to make critical choices. Thyroid patients urgently need a robust and latency-sensitive Quality of Service framework. This paper aims to integrate fog computing and artificial intelligence with smart health to provide a dependable platform for thyroid infection early detection. To identify thyroid patients, a novel ensemble-based classifier is proposed. The thyroid dataset is obtained from the UCI library and the simulation is carried out utilizing Python programming. To increase the framework's security, encryption and decryption methods are suggested. The suggested framework's performance is assessed in terms of latency, network use, RAM utilization, and energy consumption. On the other side, the suggested classifier's accuracy, precision, specificity, sensitivity and F1 score are all assessed. The result demonstrates that the suggested framework and classifier perform consistently better than conventional frameworks and classifiers.

Automated summary

The thyroid is a critical endocrine gland in regulating various bodily processes. Research on the disease's origins and spread is urgently needed. The integration of fog computing and artificial intelligence aims to provide early detection of thyroid infections, with proposed frameworks and classifiers showing better performance than traditional methods.