A comparative study of machine learning algorithms for thyroid disease classification

Thyroid disorders, affecting millions of individuals across the globe, require prompt and reliable diagnosis for optimal treatment and better patient results. On the other hand, conventional diagnostic tools are usually time-consuming and human-biased. This paper reviews an exploratory comparison of several machine learning (ML) algorithms for early diagnosis and classification of thyroid diseases based on their ability to automatize and hence the medical diagnosis. Through the comparison of the strengths and weaknesses of various ML methods, we assess them in terms of accuracy, precision, F1 score, and their applicability to clinical use. Our study utilizes datasets containing thyroid-related factors such as age, gender, TSH, T3 followed by feature selection and compares the performance of various ML techniques for thyroid disease. The purpose of this study is to contribute to the expanding literature on how machine learning can be effectively used for diagnosis enhancement of thyroid diseases and classify it into: hypothyroid, hyperthyroid, euthyroid.