Construction and evaluation of machine learning-based predictive models for early-onset preeclampsia

• Published in: Pregnancy hypertension Vol. 39; p. 101198
• Main Authors: Lv, Bohan, Wang, Gang, Pan, Yueshuai, Yuan, Guanghui, Wei, Lili
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2025
• Subjects: Adult; Algorithms; Early-onset preeclampsia; Female; Humans; Logistic Models; Machine Learning; Neural Networks, Computer; Pre-Eclampsia - diagnosis; Pre-Eclampsia - epidemiology; Predictive modeling; Predictive Value of Tests; Pregnancy; Risk Assessment; Risk Factors; Early-onset preeclampsia; Predictive modeling; Machine learning
• ISSN: 2210-7789; 2210-7797; 2210-7797
• DOI: 10.1016/j.preghy.2025.101198

•Predictive factors are general prenatal examination information that can be easily obtained on clinical big data platforms.•Five prediction models were constructed and validated through resampling.•Among the five prediction models, XGBoost algorithm performed the best predictive performance in the training and test sets. To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using machine learning algorithm. Based on Python system, the data profile of 1040 pregnant women was divided into 80% training set and 20% test set. Logistic regression algorithm, XGBoost algorithm, random forest algorithm, support vector machine algorithm and artificial neural network algorithm were used to construct the EOPE prediction model, respectively, and the resulting model was validated by resampling method. Accuracy, sensitivity, specificity, F1 score, and area under the ROC curve were used to evaluate the resulting models and screen the optimal models. EOPE in pregnant women. The results of binary logistic regression showed that the influencing factors of EOPE included six indicators: pre-pregnancy BMI, number of pregnancies, mean arterial pressure, smoking, alpha-fetoprotein, and methods of conception. Among them, the prediction model of EOPE constructed based on the XGBoost algorithm performed the best in the training and test sets, with an F1 score of 0.554 ± 0.068 and an AUC of 0.963 (95 % CI: 0.943 ∼ 0.983) in the training set, and an F1 score of 0.488 ± 0.082 and an AUC of 0.936 (95 % CI: 0.887 ∼ 0.983). Our prediction model for EOPE constructed based on the XGBoost algorithm has superior disease prediction ability and can provide assistance in predicting the disease risk of EOPE.