What are the predictive models for preeclampsia in early pregnancy?
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Machine learning-based predictive models have been developed for preeclampsia in early pregnancy. These models utilize various clinical characteristics, risk factors, routine laboratory indicators, and genetic data to predict the risk of preeclampsia . The models include logistic regression, decision tree, support vector machine (SVM), xgboost, random forest, deep neural network (DNN), naïve Bayes, and principal component analysis (PCA) . The combination of clinical variables and genetic factors in these models improves their predictive power . The accuracy of these models ranges from 70.6% to 98.6% . The SVM and random forest models have shown high accuracy rates, with an area under the receiver operating curve (AUROC) of 0.93 and 0.86, respectively . These predictive models can aid in the early detection of preeclampsia, allowing for timely intervention and improved maternal and neonatal outcomes .
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| Papers (5) | Insight |
|---|---|
| The paper does not mention any predictive models specifically for preeclampsia in early pregnancy. | |
| The paper mentions that five machine learning algorithms were used to predict preeclampsia in early pregnancy: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). | |
| The predictive models for preeclampsia in early pregnancy mentioned in the paper are decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF). | |
| The predictive models for preeclampsia in early pregnancy include xgboost and linear regression models. | |
| The paper discusses the use of logistic regression, decision tree model, and support vector machine (SVM) model as predictive models for early-onset preeclampsia in pregnant women. |
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