摘要
目的使用麻雀优化算法(SSA)优化的概率神经网络(PNN)预测妊娠风险,保证孕妇和未出生婴儿健康。方法传统的分类模型存在分类准确率不高的问题,使用麻雀优化算法(SSA)优化的概率神经网络(PNN)预测妊娠风险水平,并与一些经典的人工智能分类模型作比较。结果SSA-PNN能够更加准确地预测妊娠期间孕妇的风险水平。结论SSA-PNN模型可以为妇产科的医护人员对孕妇的健康监测行为提供决策支持。
Objective To use probabilistic neural network(PNN)optimized by sparrow search algorithm(SSA)to predict pregnancy risk and ensure the health of pregnant women and unborn babies.Methods The traditional classification model has the problem of low classification accuracy.The sparrow search algorithm(SSA)optimized probabilistic neural network(PNN)was used to predict pregnancy risk,and compared with some classical artificial intelligence classification models.Results SSAPNN was more accurate in predicting maternal risk during pregnancy.Conclusion SSA-PNN model can provide decision support for medical staff in the department of obstetrics and gynecology to monitor pregnant women's health behavior.
作者
谢小伟
曹晓丹
李晓丹
XIE Xiao-wei;CAO Xiao-dan;LI Xiao-dan(Wenzhou People's Hospital)
出处
《医院管理论坛》
2024年第10期53-55,共3页
Hospital Management Forum
关键词
妊娠风险预测
人工智能
概率神经网络
Pregnancy risk prediction
Artificial intelligence
Probabilistic neural network
