Interactive Thyroid Disease Prediction System Using Machine Learning Technique

Abstract

Thyroid disease is a major cause of formation in medical diagnosis and in the prediction, onset to which it is a difficult axiom in the medical research. Thyroid gland is one of the most important organs in our body. The secretions of thyroid hormones are culpable in controlling the metabolism. Hyperthyroidism and hypothyroidism are one of the two common diseases of the thyroid that releases thyroid hormones in regulating the rate of body’s metabolism. Data cleansing techniques were applied to make the data primitive enough for performing analytics to show the risk of patients obtaining thyroid. The machine learning plays a decisive role in the process of disease prediction and this paper handles the analysis and classification models that are being used in the thyroid disease based on the information gathered from the dataset taken from UCI machine learning repository. It is important to ensure a decent knowledge base that can be entrenched and used as a hybrid model in solving complex learning task, such as in medical diagnosis and prognostic tasks. In this paper, we also proposed different machine learning techniques and diagnosis for the prevention of thyroid. Machine Learning Algorithms, support vector machine (SVM), K-NN, Decision Trees were used to predict the estimated risk on a patient’s chance of obtaining thyroid disease.