Abstract
This paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost and EBM consistently outperform the other models. An analysis of patient subsets based on their pregnancy history was also conducted, revealing that the group of patients in their first pregnancy achieved the highest prediction accuracy. Additionally, the study explored the efficacy of risk prediction based on various parameters and found that the results vary depending on the models used and the degree of class balance in the database. Finally, an additional test was performed on the dataset annotated by physicians.
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Acknowledgements
This work has been partially supported by IFD, NCBR, and UEFISCDI in the framework of the ERA PerMed, project WODIA—Personalized Medicine Screening and Monitoring Programme for Pregnant Women Suffering from Preeclampsia and Gestational Hypertension.
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Mazur-Milecka, M. et al. (2024). Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_21
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DOI: https://doi.org/10.1007/978-3-031-38430-1_21
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