Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning

@article{monfared2026early,
  title = {{Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning}},
  author = {Vahid Monfared and Ladan Haghighi and Afkham Hashemi and Neda Farzizadeh and Zahra Najmi},
  journal = {Maternal-fetal medicine (Wolters Kluwer Health, Inc.)},
  year = {2026},
  month = jun,
  doi = {10.1097/fm9.0000000000000366},
  url = {https://europepmc.org/article/PMC/PMC13706451},
  note = {CC BY-NC-ND},
}

TY  - JOUR
TI  - Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning
AU  - Vahid Monfared
AU  - Ladan Haghighi
AU  - Afkham Hashemi
AU  - Neda Farzizadeh
AU  - Zahra Najmi
PY  - 2026
DA  - 2026/06/18
JO  - Maternal-fetal medicine (Wolters Kluwer Health, Inc.)
DO  - 10.1097/fm9.0000000000000366
UR  - https://europepmc.org/article/PMC/PMC13706451
AB  - Objective:. To develop and validate a machine learning–based model for early prediction of in-hospital mortality among preterm neonates using routinely available perinatal and neonatal clinical variables. Methods:. This retrospective single-center cohort study included 1612 live-born preterm neonates delivered between 26 and 37 weeks of gestation. Sixteen clinical maternal, perinatal, and neonatal variables were collected, including APGAR scores (appearance, pulse, grimace, activity, respiration) , need for cardiopulmonary resuscitation (CPR), gestational age, birth weight, fetal presentation, in vitro fertilization, and obstetric history. Data preprocessing included anomaly handling, multivariate imputation by chained equations (MICE), one-hot encoding/standardization, and class imbalance management using adaptive synthetic sampling and cost-sensitive learning applied to the training dataset. Model selection compared multiple classifiers, with the final model being a Random Forest identified through the Tree-Based Pipeline Optimization Tool (TPOT) automated machine learning (AutoML) pipeline. Model performance on a held-out test set was assessed using area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, likelihood ratios, and confusion matrix analysis. Feature importance was assessed using Chi-square/Cramér’s V, logistic regression coefficients, and model-based importance from Random Forest, gradient-boosted trees, and XGBoost. SHapley Additive exPlanations were used to determine the direction and magnitude of feature effects. Overfitting was mitigated through cost-sensitive learning, and consistency between training and test performance indicated minimal overfitting. Results:. The model achieved an AUC-ROC of 0.99, accuracy of 0.98, sensitivity of 0.98, specificity of 0.99, PPV of 0.99, NPV of 0.96, and F1-score of 0.988. The most influential predictors of in-hospital mortality were need for CPR, 1-minute APGAR score, gestational age < 28 weeks, 5-minute APGAR score, birth weight < 1 kg, and fetal anomalies. Conclusion:. A Random Forest–based AutoML model can accurately predict in-hospital mortality among preterm neonates using routinely available clinical variables. This data-driven tool may support earlier risk stratification, informed clinical decision-making, and improved allocation of neonatal intensive care resources in maternal-fetal and perinatal care settings.
SN  - 2096-6954
SN  - 2641-5895
ER  -