Abstract: Thyroid disorders exhibit a substantial worldwide occurrence and exert a profound influence on the health and holistic welfare of individuals. Timely identification and anticipation of thyroiditis are of paramount importance, facilitating prompt intervention and well-considered therapeutic approaches. Recent investigations have illuminated the ramifications of thyroid dysfunction on an estimated 42 million individuals in India. Imbalances in thyroid hormones give rise to hypothyroidism and hyperthyroidism, adding to the complexity of these disorders. Essential thyroid tests, such as TSH, T3, T4, and FTI, play a pivotal role in diagnosis, but the manual analysis of extensive databases poses significant challenges and demands considerable effort. In light of these obstacles, this study introduces a Machine Learning approach that utilizes the capabilities of a Decision Tree Classifier. By leveraging data patterns and relationships, the model developed demonstrates high accuracy in predicting thyroid abnormalities. The knowledge offered by the model are valuable, enhancing the understanding of thyroid diseases and aiding in the precise forecasting of these conditions. Incorporating Machine Learning methods to predict thyroiditis represents a significant advancement, Paving the way for early diagnosis and proactive management of thyroid disorders, thus positively impacting public health.

Keywords: Thyroid disorders, Machine Learning, Thyroid hormones, Decision Tree Classifier.

PDF | DOI: 10.17148/IJARCCE.2023.12705

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