Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models

Table 1 The literature summary analysis for diagnosis of thyroid illness

Ref.	Year	Dataset	Technique	Preprocessing	Validation	Performance accuracy
[17]	2023	Three datasets with 3221 patients	RF	Yes	train-test	0.91
[25]	2023	UCI Thyroid Disease data	CNN	No	train-test	0.89
[18]	2023	Thyroid dataset from GitHub repository.	SVM	Yes	cross validation	0.90
[26]	2023	UCI thyroid illness data with 7200 patients	CNN	Yes	cross validation	0.95
[27]	2023	Histopathology dataset	CNN	Yes	train-test	0.93
[25]	2023	Thyroid data obtained from Sawai Man Singh (SMS) hospital in India.	SVM	No	train-test	0.86
[28]	2022	UCI thyroid illness data with 7200 patients	RF	Yes	train-test	0.84
[19]	2022	Sick-euthyroid dataset	ANN	Yes	cross validation	0.95
[30]	2022	UCI dataset consisting of 3163 patients	MP	No	train-test	0.95

ISSN: 1472-6947