A Naive Bayes machine-learning model effectively predicted preterm birth in pregnant women with gestational diabetes and hypertensive disorders.
The study utilized electronic medical records from two hospitals in China, focusing on high-risk pregnancies with comorbid conditions.
Thirteen significant variables were identified, leading to five key predictors incorporated into the predictive model.
The Naive Bayes model demonstrated superior clinical net benefit and robustness compared to other algorithms like LASSO and Random Forest.
Future research should validate the model in larger cohorts and explore its integration into clinical decision support systems.
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