Prediction of Hypothyroidism and Hyperthyroidism Using Machine Learning Algorithms

Abstract The thyroid gland is the key organs in the human body, secreting two hormones that help to regulate the human body's metabolism. Thyroid disease is a severe medical complaint that could developed by high TSH (Thyroid Stimulating Hormone) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two important conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning model can utilize for precise processing of the data that is generated from different the medical sector and could be used for building a model for the prediction of several diseases. In this study, we used a variety of machine learning algorithm to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the preprocessing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. Finally, we found Random Forest is giving the maximum score in all sectors like accuracy, precision, recall, F1 score in our dataset and Naive Bayes is performing very poorly. By analyzing the characteristics and behavior of the dataset, we can identify the most important features of the datasets. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms for the detection and diagnosis of thyroid disease.