Thyroid conditions such as hypothyroidism and hyperthyroidism are prevalent endocrine disorders that can significantly affect metabolic function, cardiovascular health, and daily well being. Conventional diagnostic procedures, typically based on clinical assessments and laboratory evaluations, may delay early diagnosis and can be subject to human oversight. In recent developments, machine learning has emerged as a valuable aid in identifying thyroid related issues. Logistic regression, in particular, is recognized for its simplicity and transparency, making it a favored model in healthcare analytics. Variable selection techniques, including methods like SelectKBest and recursive feature elimination, assist in identifying the most relevant predictors, while approaches such as random oversampling help to address class imbalance in datasets. These practices contribute to improving the accuracy and dependability of diagnostic models, especially in nuanced or early-stage cases. Additionally, research indicates that such models may assist in fine tuning therapeutic strategies, for instance, adjusting treatment plans for individuals with hypothyroidism. Nonetheless, obstacles persist such as inconsistencies across datasets, the necessity for interpretable decision making, and integration into clinical routines. This review explores the advancements and current limitations in employing machine learning, with a focus on logistic regression, for managing and diagnosing thyroid disorders. Key Words: Thyroid Disease Prediction, Machine Learning, Ensemble Methods, Feature Selection, Class Balancing, Diagnostic Accuracy.
Thayumanavan et al. (Sat,) studied this question.