Obstetrics and Gynecology Department, Peking Union Medical College Hospital, Beijing 100730, China.
Contrast Media Mol Imaging. 2022 Aug 24;2022:5100860. doi: 10.1155/2022/5100860. eCollection 2022.
Obstetric nursing is not only complex but also prone to risks, which can have adverse effects on hospitals. Improper handling of existing risks in obstetric care can lead to enormous harm to patients and families. Therefore, it is necessary to pay attention to the risks of obstetric nursing, especially to predict the risks in a timely manner, and take effective measures to prevent them in time, so as to achieve the purpose of allowing patients to recover as soon as possible. Data mining has powerful forecasting function, so this paper proposes to combine the data-mining-based support vector machine method and XGBoost method into a forecasting model, which overcomes the shortcomings of unstable forecasting and low accuracy of a single forecasting model. The experimental results of this paper have shown that the prediction accuracy of the SVM-XGBoost combined prediction model has reached 100%, the accuracy of the single SVM prediction model is about 78%, and the accuracy of the single XGBoost prediction model is about 75%. Compared with the single SVM model and the XGBoost prediction model, the accuracy rate had increased by about 22% and 25%, and the precision rate and recall rate are also improved. Therefore, it is very suitable to use the SVM-XGBoost combined prediction model to predict the risk of obstetric nursing.
产科护理不仅复杂，而且容易出现风险，这可能对医院产生不良影响。如果产科护理中现有的风险处理不当，可能会对患者和家庭造成巨大伤害。因此，有必要关注产科护理的风险，特别是及时预测风险，并采取有效措施及时预防，以达到让患者尽快康复的目的。数据挖掘具有强大的预测功能，因此本文提出将基于数据挖掘的支持向量机方法和 XGBoost 方法相结合，形成一个预测模型，以克服单一预测模型预测不稳定和准确性低的缺点。本文的实验结果表明，SVM-XGBoost 组合预测模型的预测准确率达到 100%，单一 SVM 预测模型的准确率约为 78%，单一 XGBoost 预测模型的准确率约为 75%。与单一 SVM 模型和 XGBoost 预测模型相比，准确率提高了约 22%和 25%，且精度和召回率也有所提高。因此，使用 SVM-XGBoost 组合预测模型来预测产科护理风险是非常合适的。
