Table 1. A summary of recent publications for diagnosing thyroid disease using machine learning and deep learning approaches.

Ref	Year	Dataset	Sample Size	Target Distribution	Models	Accuracy Score
[26]	2019	Medical Facility, Kashmir	807	Imbalanced	KNN, DT, SVM, and NB	KNN: 91.82%, SVM: 96.52%, NB: 91.57&, and DT: 98.9%
[8]	2019	UCI	50	N/A	DT, SVM, and MLR	DT: 97.97%
[7]	2020	UCI	3710	Imbalanced	DT, RF, ET, and Ensemble	DT: 98%, RF: 99%, ET: 93%, and Ensemble: 100%
[27]	2021	UCI	215	Imbalanced	KNN, DT, and LR	KNN: 96.875%, DT: 87.5%, and LR: 81.25%
[9]	2021	DHQ, DG Khan, Pakistan	309	Imbalanced	SVM, KNN, DT, LR, and NB	KNN-L1: 97.84%, KNN-L2: 96.77%, DT-L1: 75.34%, DT-L2: 76.92%, NB-L1: 100%, NB-L2: 100%, SVM-L1: 86.02%, SVM-L2: 86.02%, LR-L1: 100%, and LR-L2: 98.82%
[29]	2021	Labs and Hospitals in Iraq	1250	N/A	DT, SVM, RF, NB, LR, KNN, LDA Linear Discriminant Analysis, and MLP Multi-layer Perceptron	DT: 98.4%, SVM: 92.27%, RF: 98.93%, NB: 81.33%, LR: 91.47%, LDA:83.2%, KNN:90.93%, and MLP: 97.6%
[10]	2021	UCI	7200	N/A	SVM and RF	SVM: 93% and RF: 92%
[30]	2021	DHQ, DG Khan, Pakistan	309	Imbalanced	RF, Base Meta Estimator (BME), AdaBoost, and XGBoost	Accuracy = 100%
[28]	2022	UCI	3152	Imbalanced	KNN, DNN	KNN-PCA: 94.92%, KNN-SVD: 95.72%, KNN-DT: 97.94%, DNN-PCA: 96.04%, DNN-SVD: 96.67%, DNN-DT: 98.70%, and DNN-GD: 99.95%
[31]	2022	UCI Thyroid 0387	7200	N/A	RF, BME, AdaBoost, and XGBoost	LR-RFE: 99.27%
[35]	2022	UCI	1774	Balanced	RF, GBM, LR, AdaBoost, SVM, LSTM, CNN, CNN-LSTM	RF-MLFS: 99%