Assessment of Classifiers with Machine Learning based Thyroid Disease Prediction System
Naveen R Chanukotimath1, Sunil Kumar B S2
1Mr. Naveen R Chanukotimath*, Department of ISE, GM Institute of Technology, Davangere, India.
2Dr. Sunil Kumar B S, Department of ISE, GM Institute of Technology, Davangere, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4718-4721 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6887018520/2020©BEIESP | DOI: 10.35940/ijrte.E6887.018520
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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: Most of the people in different nations are suffering from Thyroid related diseases and these are lifelong. Many people are unaware of having Thyroid related diseases. Main cause for this is due to improper functioning of Thyroid gland secreting Thyroid hormone which regulates body metabolism. In this paper we have made survey on classifiers like Decision Tree C4.5(J48), Multilayer Perceptron, Naïve Bayes by measuring TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, ROC Area, PRC Area and developed a prediction system for Thyroid diseases. For training and testing the classifiers we have used Thyroid dataset from UCI repository. Dataset consists of 9172 records containing 29 attribute values and 1 diagnosis class value. The diagnosis class value consists of different types Thyroid disease conditions like hyperthyroid conditions, hypothyroid conditions, binding protein, general health, replacement therapy, antithyroid treatment and miscellaneous. The proposed prediction system model capable of predicting type of Thyroid disease whether a person is suffering or not.
Keywords: Decision Tree C4.5 (J48), Evaluation, Multilayer Perceptron, Naïve Bayes, Thyroid.
Scope of the Article: Nondestructive Testing and Evaluation.