Abstract
Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects of thyroid syndrome extend beyond the thyroid itself, affecting metabolism, energy levels, and overall well-being. Thyroid syndrome is associated with severe cases of thyroid dysfunction, highlighting the potentially life-threatening consequences of untreated or inadequately managed thyroid disorders. This research aims to propose an advanced meta-learning approach for the timely detection of Thyroid syndrome. We used a standard thyroid-balanced dataset containing 7,000 patient records to apply advanced machine-learning methods. We proposed a novel meta-learning model based on a unique stack of K-Neighbors (KN) and Random Forest (RF) models. Then, a meta-learning Logistic Regression (LR) model is built based on the collective experience of stacked models. For the first time, the novel proposed KRL (KN-RF-LR) method is employed for the effective diagnosis of Thyroid syndrome. Extensive research experiments illustrated that the novel proposed KRL outperformed state-of-the-art approaches, achieving an impressive performance accuracy of 98%. We vindicated the performance scores through k-fold cross-validation and enhanced performance using hyperparameter tuning. Our research revolutionized the timely detection of thyroid syndrome, contributing to the enhancement of human life by reducing thyroid mortality rates.