Thyroid diseases are instigated due to disparity in production of hormones –TSH, T4 and T3. Most of the patients of thyroid dysfunction go untreated due to late detection or no detection at all. Machine learning based models for detection of thyroid diseases offer a significant assistance to healthcare. The medical history of the patient supplies the features required by machine learning based classification and prediction models for thyroid dysfunction. The aim of this research paper is to acquire a classification model based on machine learning techniques for assessment of euthyroidism, hyperthyroidism, and hypothyroidism among males, females, and children. Different machine learning classification algorithms such as naïve bayes, decision tree, random forest and logistic regression are used for classification of real data. The accuracy of each of the techniques has established using metrics like precision, recall, specificity and sensitivity. A thyroid dataset has been retrieved from two hospitals in Haryana from January 2020 to July 2020 to train the proposed model. The dataset comprises of medical history of 539 thyroid patients including children, men, and women of various ages. Out of 539 patients screened, 163 have irregular TSH, 138 have prevalence of elevated TSH with 376 having minimal TSH elevation.

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The thyroid seems to be an part of the endocrine system that is placed toward the front of neck and produces thyroxine, which are essential for our overall health. If it fails, thyroid hormone production will either be insufficient or excessive. Machine learning techniques and data mining are critical in processing large amounts of data, particularly in the health care system, where there has been a massive amount of information and data need to be managed. In our research on thyroid disease, we used machine learning approaches. In our study, we used statistics from patients, a few of which has hyperactive thyroid glands moreover those have hypothyroidisms; therefore, overall algorithms were used. These study aims to divide this disease in few categories like as hypothyroidism, regular and hyperthyroidism. Support vector machine include KNN, naive-bayes, logistic regressions, decision tree, random forest, discriminant function analysis, and multilayer perceptron (MLP). To the thyroid diseases classification.

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