Towards an Explainable AI-Based Tool to Predict Preterm Birth

Kokkinidis, I.; Logaras, Evangelos; Rigas, Emmanouil S.; Tsakiridis, Ioannis; Dagklis, Themistoklis; Billis, Antonios; Bamidis, Panagiotis D.

doi:10.3233/shti230207

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Fourth, we did not have information on the length of the uterine cervix, which is a known predictor of PTB. Fifth, although we used hybrid and under-sampling methods in the training data set to improve model performance, we did not balance the validation and testing sets to assess model performance, as some previous studies did (Nieto-Del-Amor et al, 2022;Kyparissidis Kokkinidis et al, 2023). Finally, there may have been misclassification and selection bias in our electronic health record-based study.…”

Section: Discussionmentioning

confidence: 99%

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms

Yu,

Lin,

Zhou

et al. 2024

Front. Big Data

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We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.

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Section: Discussionmentioning

confidence: 99%