02176nas a2200205   4500000000100000000000100001008004100002260001200043653001300055653002400068653002100092653002000113100001600133245013100149856005800280300001000338490000600348520160200354022001401956       2024                            d  c09/202410aFixMatch10aIoE Medical Systems10aMachine Learning10aThyroid Disease1 aMelih Agraz00aSemi-Supervised Machine Learning Approaches for Thyroid Disease Prediction and its Integration With the Internet of Everything  uhttps://www.ijimai.org/journal/bibcite/reference/3469  a38-470 v83 aThyroid disorders are critical conditions that considerably affect a person’s general health, and may lead to additional health complications. Notably, these conditions often remain undetected in individuals who show "normal" results on traditional thyroid function tests. To enhance the diagnostic accuracy for thyroid disorders, such as hypothyroidism and hyperthyroidism, this study leveraged digital health records and explored semisupervised learning methods. We intentionally removed the labels from subjects initially categorized as "normal," incorporating them into our dataset as unlabeled data. The goal was to overcome the limitations of conventional diagnostic techniques, which may fail to detect subtle imbalances in thyroid hormones. In pursuit of this objective, we employed a combination of semi-supervised learning methods, namely FixMatch, Co-training, and self-training, in conjunction with supervised learning algorithms, specifically Naive Bayes and logistic regression. Our findings indicate that the FixMatch algorithm surpassed traditional supervised learning methods in various metrics, including accuracy (0.9054), sensitivity (0.9494), negative predictive value (0.9365), and F1 score (0.9146). Additionally, we propose a framework for integrating these diagnostic tools into the Internet of Everything (IoE) to promote early detection and facilitate improved healthcare outcomes. This research highlights the potential of semi-supervised learning techniques in the diagnosis of thyroid disorders and offers a roadmap for harnessing the IoE in healthcare advancement.  a1989-1660