Abstract: The thyroid gland, one of the body’s key endocrine organs, produces two essential hormones that regulate metabolic activity. Abnormal functioning of this gland can lead to disorders such as hypothyroidism and hyperthyroidism, both of which significantly disrupt the body's normal physiological processes. Although thyroid disorders are generally diagnosed through blood tests, these tests often yield ambiguous or noisy results, making accurate diagnosis difficult. To address this challenge, the present study incorporates data cleaning methods and machine learning techniques to enhance the accuracy of thyroid disease detection and prediction. Clean and structured data improved the reliability of the analysis. Various machine learning algorithms, including logistic regression, decision trees, k-nearest neighbors (KNN), support vector machines (SVM), XG Boost, and artificial neural networks (ANN), were employed to model and predict.
Keywords: hypothyroidism, hyperthyroidism, diagnosed, machine learning algorithms decision tree, KNN, SVM, ANN
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DOI:
10.17148/IJARCCE.2025.14733
