Revealing Insights: Machine Learning-Based Prediction of Thyroid Disorders
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Abstract
This paper is crafted as a valuable reference for research scholars delving into the realm of thyroid disease prediction. In our pursuit of predicting and assessing the effectiveness of diverse machine learning techniques, we extensively employed three prominent algorithms: logistic regression, decision trees, and k- nearest neighbor (kNN) algorithms. This study encapsulates the essence of thyroid disease prediction, shedding light on the practical application of logistic regression, decision trees, and kNN as powerful classification tools. The analysis leveraged the Thyroid dataset from the UC Irvine Knowledge Discovery in Databases Archive, contributing to a comprehensive exploration of this vital healthcare concern.
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