THYROID DISEASE CLASSIFICATION USING MACHINE LEARNING

Thyroid disease is a common endocrine disorder that affects millions of people worldwide. Nowadays most of the women suffering from thyroid disease than male. There are two types in thyroid disease like hypothyroid and hyperthyroid disease. These diseases giving many side effects such as weight gain, weight loss, stress and so on to our human body. If this disease is detected in earlier stage, then physician can give proper treatment to the patients. Existing methods for thyroid detection, such as manual palpation and ultrasonography, have limitations in terms of sensitivity, specificity, and accuracy. Accurate diagnosis and classification of thyroid disease are critical for appropriate treatment and management of the disease. Machine learning techniques have emerged as promising tools for the automatic classification of thyroid diseases based on clinical, laboratory, and imaging features. In this paper, we review recent studies on thyroid disease classification using machine learning techniques. We summarize the common machine learning algorithms used for thyroid disease classification decision tree, support vector machines, XG Boost, K-nearest neighbours and AdaBoost. Overall, the studies suggest that machine learning techniques can achieve high accuracy in the classification of thyroid diseases, with some models achieving performance levels that are comparable to or better than those of human experts. However, further research is needed to validate the accuracy and generalizability of these models in large and diverse patient populations.