ISSN: 1007-1172

Impact Factor: 6.2


Journal of Shanghai Jiaotong University

International Peer Reviewed | Open Access | A Monthly Publishing Journal

"Supervised Learning Algorithms for Classifying Thyroid Disease"


Ashish Kumar Sen, Research Scholar
AwadheshPratap Singh University,Rewa (M.P)-India.

Dr.Prabhat Pandey, Professor
Physics & O.S.D., Add. Directorate Office,Higher Education Rewa (M.P.)-India.


With the enormous amount of data and information that must be processed, particularly in the health system, machine learning algorithms and data mining techniques play a critical role in data management. We employed machine learning techniques to examine thyroid disorders in our study. The purpose of this study is to classify thyroid disease into three categories: hyperthyroidism, hypothyroidism, and normal. To accomplish this, we used data from Iraqi citizens, some of whom have hyperthyroidism and others who have hypothyroidism. Support vector machines, random forest, decision tree, naive bayes, logistic regression, k nearest neighbours, multilayer perceptron (MLP), and linear discriminant analysis are all terms that refer to support vector machines.