Development of machine learning models for predicting early pregnancy outcomes based on β-hCG, progesterone, and estradiol

by Luming Chen, Fangxiang Mu, Kexin Wang, Fang Wang Objective This study aims to develop a machine learning model for predicting early pregnancy outcomes by combining baseline levels and dynamic changes of β-human chorionic gonadotropin (β-hCG), progesterone (P), and estradiol (E2). Methods This retrospective study screened out 421 patients treated at the Lanzhou University Second Hospital between March 2023 and August 2024.

Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest Recursive Feature Elimination (RF-RFE). Subsequently, we constructed a traditional logistic regression model and five machine learning models: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) neural network, and Support Vector Machine (SVM).

Internal validity was assessed through 5-fold cross-validation. Model performance was measured by the area under the Receiver Operating Characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.

Results Among the 421 enrolled patients, 263 had ongoing pregnancies while 158 experienced early pregnancy loss (EPL).