A comparative study on thyroid disease diagnosis using neural networks

Thyroid hormones produced by the thyroid gland help regulation of the body’s metabolism. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Thyroid disease diagnosis via proper interpretation of the thyroid data is an important classification problem. In this study, a comparative thyroid disease diagnosis were realized by using multilayer, probabilistic, and learning vector quantization neural networks. For this purpose, thyroid disease dataset which is taken from UCI machine learning database was used.