• Niharikareddy Meenigea

This paper is being written to serve as a resource for research scholars interested in the prediction of thyroid disease. To predict and evaluate the performance of different machine learning techniques, three algorithms were widely used, namely logistic regression, decision trees, and k- nearest neighbour (kNN) algorithms. This study represented the intuition of how to predict thyroid disease and highlighted how to use logistic regression, decision trees, and kNN as classification tools. Thyroid data set of machine learning repository from UC Irvin knowledge discovery in databases archive was used for this.

Decision Tree, Thyroid Disease, K-nearest neighbour, Logistic Regression

"Unveiling the Hidden Truth: Predicting Thyroid Disease using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.f475-f486, August-2021, Available :http://www.jetir.org/papers/JETIR2108663.pdf

"Unveiling the Hidden Truth: Predicting Thyroid Disease using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppf475-f486, August-2021, Available at : http://www.jetir.org/papers/JETIR2108663.pdf