Computational way of Classifying Thyroid Disorder Patients and Identifying the Similar Patients using a Novel Method
S. Jamuna1, K. Mohan Kumar2
1S.Jamuna*, Research Scholar, PG and Research Department of Computer Science, Rajah Serfoji Government College, Thanjavur, Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
2Dr. K. Mohan Kumar, Head, PG and Research Department of Computer Science, Rajah Serfoji Government College, Thanjavur, Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 24, 2020. | Manuscript published on March 10, 2020. | PP: 432-438 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2467039520/2020©BEIESP | DOI: 10.35940/ijitee.E2467.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The thyroid hormones secreted by thyroid gland are interrelated with many metabolic processes of our body. Any dysfunction of thyroid gland leads to thyroid diseases. Hypothyroidism and hyperthyroidism are the very common thyroid disorders which affect the large number of people nowadays. Prediction of thyroid diseases at right time and giving suitable medicines to the patients help them to overcome the health problems. A machine learning technique will definitely assist the physicians for the prediction and treatment of thyroid diseases. In this work, the datasets are taken from UCI repository and Fuzzy- C Means algorithm is used for the clustering the thyroid diseases.
Keywords: Cluster Analysis, Clustering, Thyroid, Fuzzy C-Means, Hypothyroidism, Hyperthyroidism and Prediction.
Scope of the Article: Clustering