مقاله انگلیسی اینترنت اشیا برای حفظ یک سیستم مراقبت بهداشتی هوشمند و ایمن
Abstract
Keywords
Introduction
Related work
Proposed framework
Security aspects of proposed framework
Experimental setup and performance evaluation
Conclusion
CRediT authorship contribution statement
References
Abstract
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.
Introduction
Thyroid disease is a chronic condition that affects almost 200 million people globally and is more prevalent in women than men. Thyroid disorder in women is difficult to diagnose and treat due to a variety of causes. Many women with thyroid disease are misdiagnosed or continue to have symptoms even after being treated. Numerous causes complicate the diagnosis and treatment of thyroid disease. The most often used blood test for thyroid disorder (thyroid-stimulating hormone [TSH]) can yield false results. More precisely, while combining all three major thyroid function tests (TSH, free T3, and free T4) provides the most comprehensive assessment of thyroid function, the TSH test is often used alone. As a result, critical health diagnoses (e.g., hyperthyroidism, hypothyroidism) are sometimes overlooked. Additionally, since thyroid dysfunction mimics the effects of other diseases (e.g., stress, menopause), thyroid disease is often misdiagnosed and untreated. Today’s age of ICT IoT, cloud computing, fog computing, and artificial intelligence has accomplished numerous marvels that human beings could not have imagined.