BACKGROUND
International guidelines recommend early screening based on targeted risk factors to identify thyroid disease during pregnancy. The complexity of these risk factors makes accurate prediction challenging. This study aimed to develop and compare multiple machine-learning-based predictive models for thyroid disease during pregnancy.
MATERIAL AND METHODS
This retrospective study analyzed the clinical characteristics of 5461 women who gave birth at a single center. The dataset was divided into training and test sets. In the training set, feature variables associated with thyroid disease during pregnancy were selected using the Boruta algorithm. Eight models were developed: logistic regression, Bayesian approach, k-nearest neighbors, support vector machine, neural network, classification and regression tree, extreme gradient boosting, and random forest (RF). Model performance was evaluated using the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), calibration curve, and decision curve analysis.
RESULTS
Nine feature variables were identified: age, height, pre-pregnancy weight, gravidity, parity, primiparity or multiparity, hypertensive disorders of pregnancy, scarred uterus, and autoimmune disease. The RF model demonstrated the best performance, with accuracy of 0.98387819 and 0.99597990, Matthews correlation coefficient of 0.96794139 and 0.97781292, log loss of 0.12670703 and 0.09442025, Brier score of 0.02495798 and 0.01921069, area under the ROC curve of 0.99877170 and 0.99991140, and area under the PRC of 0.99864486 and 0.99922572 in the training and test sets, respectively.
CONCLUSIONS
The RF model demonstrates excellent discriminative performance, accuracy, consistency, and generalizability in predicting thyroid disease during pregnancy.

Keywords: Machine Learning; Predictive Learning Models; Pregnancy Complications; Random Forest; Thyroid Diseases