A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia
Yanhong Xu,1,* Yizheng Zu,2,* Ying Zhang,1,* Zewei Liang,1 Xia Xu,1,3– 5 Jianying Yan1,3– 5 1College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, Peop...
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Dove Medical Press
2025-08-01
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| Series: | International Journal of General Medicine |
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| author | Xu Y Zu Y Zhang Y Liang Z Xu X Yan J |
| author_facet | Xu Y Zu Y Zhang Y Liang Z Xu X Yan J |
| author_sort | Xu Y |
| collection | DOAJ |
| description | Yanhong Xu,1,&ast; Yizheng Zu,2,&ast; Ying Zhang,1,&ast; Zewei Liang,1 Xia Xu,1,3– 5 Jianying Yan1,3– 5 1College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 2The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 3Fujian Clinical Research Center for Maternal-Fetal Medicine, Fuzhou, Fujian, People’s Republic of China; 4Laboratory of Maternal-Fetal Medicine, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 5National Key Obstetric Clinical Specialty Construction Institution of China, Fuzhou, Fujian, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xia Xu, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email xuxia0623@fjmu.edu.cn Jianying Yan, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email yanjy2019@fjmu.edu.cnPurpose: To analyze the risk factors for preterm birth in patients with early-onset preeclampsia (EOPE) based on multi-algorithm machine learning and to construct a predictive model to explore the predictive value of the model.Methods: A retrospective analysis was conducted on 442 EOPE patients from a single tertiary center, divided into preterm birth (< 37 weeks, n=358) and term-born (≥ 37 weeks, n=84) groups. Univariate analysis, random forest importance assessment, lasso regression combined with multivariate regression analysis were used for feature evaluation. Eight machine learning models were trained (70% data) and validated (30% data). A Stacking ensemble model was constructed, and SHapley Additive exPlanations (SHAP) was used for feature interpretation.Results: The area under the receiver operating characteristic curve (AUROC) for predicting preterm birth in EOPE patients using Logistic Regression, Gaussian Naive Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multi-Layer Perceptron, and Elastic Net were 0.763, 0.712, 0.821, 0.832, 0.821, 0.842, 0.784, and 0.763, respectively. The Stacking model (XGBoost+GBDT+SVM) achieved superior performance (AUROC=0.865). Three independent risk factors were identified: fetal growth restriction (aOR=3.50, p = 0.047), serum cystatin C (aOR=11.27, p = 0.018), and C-reactive protein (aOR=1.37, p < 0.001). SHAP analysis revealed GBDT as the top contributor to Stacking predictions, with microalbunminuria (GBDT, XGBoost) and age (SVM) being the most influential features.Conclusion: Machine learning models can serve as reliable assessment tools for predicting the risk of preterm birth in patients with EOPE. The ensemble prediction model demonstrates the best predictive performance, helping obstetricians identify high-risk patients and perform early intervention to improve perinatal outcomes.Keywords: machine learning, preterm birth, early-onset preeclampsia, clinical prediction model |
| format | Article |
| id | doaj-art-34b6c8b17d4c4bc689ffbf1a1951e925 |
| institution | Kabale University |
| issn | 1178-7074 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | International Journal of General Medicine |
| spelling | doaj-art-34b6c8b17d4c4bc689ffbf1a1951e9252025-08-20T03:44:01ZengDove Medical PressInternational Journal of General Medicine1178-70742025-08-01Volume 18Issue 141954207105449A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset PreeclampsiaXu Y0Zu Y1Zhang Y2Liang Z3Xu X4Yan J5College of Clinical Medicine for Obstetrics & Gynecology and PediatricsCollege of Clinical Medicine for Obstetrics & Gynecology and PediatricsCollege of Clinical Medicine for Obstetrics & Gynecology and PediatricsCollege of Clinical Medicine for Obstetrics & Gynecology and PediatricsCollege of Clinical Medicine for Obstetrics & Gynecology and PediatricsCollege of Clinical Medicine for Obstetrics & Gynecology and PediatricsYanhong Xu,1,&ast; Yizheng Zu,2,&ast; Ying Zhang,1,&ast; Zewei Liang,1 Xia Xu,1,3– 5 Jianying Yan1,3– 5 1College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 2The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 3Fujian Clinical Research Center for Maternal-Fetal Medicine, Fuzhou, Fujian, People’s Republic of China; 4Laboratory of Maternal-Fetal Medicine, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 5National Key Obstetric Clinical Specialty Construction Institution of China, Fuzhou, Fujian, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xia Xu, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email xuxia0623@fjmu.edu.cn Jianying Yan, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email yanjy2019@fjmu.edu.cnPurpose: To analyze the risk factors for preterm birth in patients with early-onset preeclampsia (EOPE) based on multi-algorithm machine learning and to construct a predictive model to explore the predictive value of the model.Methods: A retrospective analysis was conducted on 442 EOPE patients from a single tertiary center, divided into preterm birth (< 37 weeks, n=358) and term-born (≥ 37 weeks, n=84) groups. Univariate analysis, random forest importance assessment, lasso regression combined with multivariate regression analysis were used for feature evaluation. Eight machine learning models were trained (70% data) and validated (30% data). A Stacking ensemble model was constructed, and SHapley Additive exPlanations (SHAP) was used for feature interpretation.Results: The area under the receiver operating characteristic curve (AUROC) for predicting preterm birth in EOPE patients using Logistic Regression, Gaussian Naive Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Multi-Layer Perceptron, and Elastic Net were 0.763, 0.712, 0.821, 0.832, 0.821, 0.842, 0.784, and 0.763, respectively. The Stacking model (XGBoost+GBDT+SVM) achieved superior performance (AUROC=0.865). Three independent risk factors were identified: fetal growth restriction (aOR=3.50, p = 0.047), serum cystatin C (aOR=11.27, p = 0.018), and C-reactive protein (aOR=1.37, p < 0.001). SHAP analysis revealed GBDT as the top contributor to Stacking predictions, with microalbunminuria (GBDT, XGBoost) and age (SVM) being the most influential features.Conclusion: Machine learning models can serve as reliable assessment tools for predicting the risk of preterm birth in patients with EOPE. The ensemble prediction model demonstrates the best predictive performance, helping obstetricians identify high-risk patients and perform early intervention to improve perinatal outcomes.Keywords: machine learning, preterm birth, early-onset preeclampsia, clinical prediction modelhttps://www.dovepress.com/a-multi-algorithm-machine-learning-model-for-predicting-the-risk-of-pr-peer-reviewed-fulltext-article-IJGMmachine learningpreterm birthearly-onset preeclampsiaclinical prediction model |
| spellingShingle | Xu Y Zu Y Zhang Y Liang Z Xu X Yan J A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia International Journal of General Medicine machine learning preterm birth early-onset preeclampsia clinical prediction model |
| title | A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia |
| title_full | A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia |
| title_fullStr | A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia |
| title_full_unstemmed | A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia |
| title_short | A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia |
| title_sort | multi algorithm machine learning model for predicting the risk of preterm birth in patients with early onset preeclampsia |
| topic | machine learning preterm birth early-onset preeclampsia clinical prediction model |
| url | https://www.dovepress.com/a-multi-algorithm-machine-learning-model-for-predicting-the-risk-of-pr-peer-reviewed-fulltext-article-IJGM |
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