Review of Data Mining Algorithms for Thyroid Disease Classification

Abstract

Thyroid disease classification plays a pivotal role in early diagnosis and effective management of thyroid disorders. This research paper presents a comprehensive review of data mining algorithms employed in the context of thyroid disease classification. The study systematically examines various classification techniques, including Decision Trees, k-Nearest Neighbors, Support Vector Machines, Neural Networks, and ensemble methods like Random Forest and AdaBoost. Through a critical evaluation of existing literature, we analyze the strengths, limitations, and comparative performances of these algorithms in the domain of thyroid disease detection. The review aims to provide a nuanced understanding of the current landscape of data mining methodologies applied to thyroid disease classification, shedding light on the evolving trends, challenges, and potential avenues for future research. By synthesizing insights from diverse studies, this review contributes to the ongoing discourse on optimizing diagnostic tools for thyroid disorders, ultimately paving the way for more accurate and efficient healthcare interventions.