The world is facing an increase in the incidence of thyroid diseases, including in Jordan as well, especially hypothyroidism. This condition can be diagnosed through blood samples to know the percentage of hormones (TSH, T3, and T4) and take the appropriate decision after that. The major goal of this study is to make diagnosis easier and reduce the risk of misdiagnosis, which happens all too often. Methods: The use of machine learning in the prediction of disease is a crucial step in the process of data analysis. This paper presents an analysis and classification model that takes into account the various factors involved in the prediction of disease. We applied a number of machine learning algorithms like support vector machine (SVM), Nave Bayes, decision trees, and ensemble to get the best prediction. The decision tree algorithm showed the highest accuracy and lowest cost with a percentage of 97.6 percent and 90, respectively. The results appeared in the form of three classes (compensated hypothyroidism, primary hypothyroidism, and negative). Conclusion: The best results were obtained at the milestone point in computer-aided diagnosis of hypothyroidism. Future work is considered to increase the number of cases that can be detected and diagnosed using machine learning.
