NDLI: Building a machine learning-based risk prediction model for second-trimester miscarriage

Content Provider	Springer Nature : BioMed Central
Author	Qi, Sangsang Zheng, Shi Lu, Mengdan Chen, Aner Chen, Yanbo Fu, Xianhu
Abstract	Background Second-trimester miscarriage is a common adverse pregnancy outcome that imposes substantial economic and psychological pressures on both the physical and mental well-being of patients and their families. Currently, there is a scarcity of research on predictive models for the risk of second-trimester miscarriage. Methods Clinical data were retrospectively collected from patients who were in the second trimester of pregnancy (between 14+0 and 27+6 weeks gestation), whose main diagnosis was “threatened abortion” and who were hospitalized at the Women and Children’s Hospital of Ningbo University from January 2020 to October 2023. Following preliminary data processing, the patient cohort was randomly stratified into a training cohort and a validation cohort at proportions of 70% and 30%, respectively. The Boruta algorithm and multifactor analysis were used to refine feature factors and determine the optimal features linked to second-trimester miscarriages. The imbalanced dataset from the training cohort was rectified by applying the SMOTE oversampling approach. Seven machine-learning models were built and subjected to a comprehensive analysis to validate and evaluate their predictive capabilities. Through this rigorous assessment, the optimal model was selected. Shapley additive explanations (SHAP) were generated to provide insights into the model’s predictions, and a visual representation of the predictive model was built. Results A total of 2006 patients were included in the study; 395 (19.69%) of them had second-trimester miscarriages. XGBoost was shown to be the optimal model after a comparison of seven different models utilizing metrics such as accuracy, precision, recall, the F1 score, precision-recall average precision, the receiver operating characteristic-area under the curve, decision curve analysis, and the calibration curve. The most significant feature was cervical length, and the top ten features of second-trimester miscarriage were found using the SHAP technique based on relevance rankings. Conclusion The risk of a second-trimester miscarriage can be accurately predicted by the visual risk prediction model, which is based on the machine learning mentioned above.
Related Links	https://bmcpregnancychildbirth.biomedcentral.com/counter/pdf/10.1186/s12884-024-06942-w.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14712393
DOI	10.1186/s12884-024-06942-w
Journal	BMC Pregnancy and Childbirth
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-11-09
Access Restriction	Open
Subject Keyword	Reproductive Medicine Maternal and Child Health Gynecology Second-trimester miscarriage Machine learning Prediction models
Content Type	Text
Resource Type	Article
Subject	Obstetrics and Gynecology
Journal Impact Factor	2.8/2023
5-Year Journal Impact Factor	3.4/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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