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@article{175324,
        author = {S.Dollar Venkata Ramana Deekshith and M.Sai Pavan and S.Joseph Reddy and Sk.Sadik and S.Ramadoss},
        title = {Predection of thyroid disease using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3636-3644},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175324},
        abstract = {Thyroid medical diagnosis and prediction development, which medical science is a complicated axiom. Thyroid gland is one of our body's main organs. Thyroid hormone secretions are responsible for regulating metabolism. Hyperthyroidism and hypothyroidism are the two prominent thyroid disorders that produce thyroid hormones for control of body metabolism. The machine learning is critical in the disease prediction process and in the study and classification models used for thyroid disease on the basis of data obtained from hospital datasets. A decent knowledge base must be ensured, built and used as a hybrid model to solve dynamic learning tasks like medical diagnosis and prediction tasks. Basic techniques of machine learning are used for the identification and inhibition of thyroid. The SVM is used to predict the approximate probability of a thyroid patient. If the patient has risk of getting thyroid our system has to give suggestions like recommending home remedies, precautions, medication secretions are responsible for regulating metabolism. Hyperthyroidism and hypothyroidism are the two prominent thyroid disorders that produce thyroid hormones for control of body metabolism. The machine learning is critical in the disease prediction process and in the study and classification models used for thyroid disease on the basis of data obtained from hospital datasets.decent knowledge base must be ensured, built and used as a hybrid model to solve dynamic learning tasks like medical diagnosis and prediction tasks. Basic techniques of machine learning are used for the identification and inhibition of thyroid. The SVM is used to predict the approximate probability of a thyroid patient. If the patient has risk of getting thyroid our system has to give suggestions like recommending home remedies, precautions, medication etc.Thyroid disorders, such as hypothyroidism, hyperthyroidism, and thyroid cancer, pose significant health challenges worldwide},
        keywords = {Machine Learning Algorithm, Thyroid disease, Support Vector Machine (SVM), K-NN, Decision Trees Prediction},
        month = {April},
        }