Thyroid Carcinoma Prediction Using ACO and Machine Learning Techniques

The thyroid gland, a butterfly-shaped nodule in the front of the neck, can develop thyroid carcinoma. Compared to other carcinomas, thyroid carcinoma is one of the most common endocrine carcinomas. Thyroid carcinoma may not create symptoms at first, but it can cause soreness and enlargement in the forward facing of the neck as it spreads. In this study, first thyroid disease is identified and later-on on the basis of symptoms such as Neck Swelling, TSH, Tumor, Neck Pain, Anxiety Thyroid Carcinoma is identified. Over the past few decades, patients with thyroid disease and carcinoma have been on the rise. Thyroid disease is the second most prevalent malignancy; the majority of instances in the general population is benign and malignant in thyroid cancer. As clinical detection of thyroid cells necessitates the utilization of various features in a variety of scales, a traditional method of extraction of features may not provide adequate results. The goal of this paper is to use ant colony optimization algorithm to reduce the number of features scores and to predict accuracy on thyroid carcinoma from the symptoms of thyroid diseases.