Comprehensive Machine Learning Approach for Early Detection and Classification of Hyperthyroidism and Hypothyroidism

Thyroid disorders, including hyperthyroidism and hypothyroidism, pose significant health risks and necessitate early detection for effective management. In this study, we present a comprehensive approach employing various machine learning algorithms for the prediction of thyroid conditions. The algorithms investigated include Random Forest, Naive Bayes, Decision Tree, Gradient Boosting, Logistic Regression, Adaboost, Support Vector Machines (SVM), and k-Nearest Neighbors (KNN). Using a dataset comprising diverse thyroid-related features, we conducted an extensive comparative analysis of these algorithms. Our findings reveal that Random Forest exhibited the highest predictive accuracy, reaching an impressive 98.46%. This outstanding performance positions Random Forest as a robust model for accurate thyroid disorder classification. The target classes encompass hyperthyroid, hypothyroid, and negative instances, providing a comprehensive framework for diagnosing thyroid conditions. The incorporation of multiple algorithms ensures the robustness of our predictive model, accommodating diverse data patterns associated with thyroid abnormalities. Our research provides a foundation for the development of an intelligent diagnostic system. As the field of machine learning progresses, there is a growing recognition of its capacity to advance healthcare diagnostics, specifically in the early identification of abnormalities related to the thyroid. This has the potential to significantly enhance patient outcomes and overall quality of life.