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The thyroid hormone is produced by thyroid gland. This hormone regulates the body's metabolism. Hyperthyroidism and hypothyroidism are the two abnormalities which is caused by the release of too much or too little thyroid hormones respectively. In this study, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (K-NN) classifiers are compared to assess the efficiency of these classifiers in Thyroid disease diagnoses using the thyroid disease dataset that is taken from UCI machine learning repository. The overall classification accuracy of the RF, SVM, and K-NN are 98.50%, 97.02%, and 95.81% respectively. The result shows that the RF classifier performance is better than SVM and K-NN for the diagnosis of thyroid disease using UCI dataset.