Machine Learning Approaches for Early Diagnosis and Prediction of Fetal Abnormalities

机译：机器学习早期诊断和预测胎儿异常的方法

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Fetal Health denotes the health and growth of the fetal and frequent contacts in the uterus of the pregnant women during pregnancy. Maximum pregnancy period complexities leads fetal to a severe difficulty which limits right growth that causes deficiency or death. Harmless pregnancy period by predicting the risk levels before the occasion of difficulties boost right fetal growth. Forecasting the fetal health and growth state from a set of pre-classified patterns knowledge is vital in developing a predictive classifier model using Machine Learning Algorithms.

机译：胎儿健康表示怀孕期间孕妇子宫内胎儿和频繁接触的健康和生长。最大妊娠期复杂性导致胎儿陷入严重的困难，这限制了导致缺陷或死亡的正确增长。通过预测困难困难之前的风险水平促进胎儿生长的无害妊娠期。从一组预先分类的模式知识预测胎儿健康和生长状态对于使用机器学习算法开发预测分类器模型至关重要。

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《International Conference on Computer Communication and Informatics》|2021年|1-3|共3页
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R. Chinnaiyan; Stalin Alex;
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Pregnancy; Machine learning algorithms; Machine learning; Predictive models; Complexity theory; Forecasting; Informatics;

机译：怀孕;机器学习算法;机器学习;预测模型;复杂性理论;预测;信息学;

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6. Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study [O] . Stefan Kuhle, Bryan Maguire, Hongqun Zhang, 2018

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7. Counselling following diagnosis of a fetal abnormality: the differing approaches of obstetricians, clinical geneticists, and genetic nurses. [O] . Marteau, T, Drake, H, Bobrow, M 1994

机译：胎儿异常诊断后的咨询：产科医生，临床遗传学家和遗传护士的不同方法。

Machine Learning Approaches for Early Diagnosis and Prediction of Fetal Abnormalities

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