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BACKGROUND Preeclampsia and intrauterine growth restriction are placental dysfunction-related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed from datasets with highly imbalanced classes. OBJECTIVE In this study, we developed a predictive model of PDDs by machine learning that uses features at 24-37 weeks' gestation, including maternal

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