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June 20, 2026Maternal-Fetal MedicineOpen Access

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

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Authors

VMVahid MonfaredLHLadan HaghighiAHAfkham Hashemi

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Overview

Randomized trial reveals a machine learning model that predicts in-hospital mortality for preterm neonates, suggesting improved clinical decisions.

Key Points

  • To develop and validate a machine learning model for early prediction of in-hospital mortality among preterm neonates using clinical variables.
  • Retrospective single-center cohort study with 1612 preterm neonates (26-37 weeks gestation).
  • Collected 16 clinical variables including APGAR scores, birth weight, and CPR need.
  • Model selection employed Random Forest via Tree-Based Pipeline Optimization Tool for enhanced prediction capabilities.
  • Achieved AUC-ROC of 0.99, accuracy of 0.98, and sensitivity of 0.98.
  • Identified CPR need, low APGAR scores, gestational age < 28 weeks, and low birth weight as key predictors.
  • Demonstrated minimal overfitting with high consistency between training and test performance.

Cite This Study

Monfared et al. (2026) studied this question.

synapsesocial.com/papers/6a362ebddb0793dc1a536599https://doi.org/10.1097/fm9.0000000000000366
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