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Background <p>Gestational anemia significantly elevates the risk of adverse maternal and neonatal outcomes, necessitating early predictive tools for targeted intervention. This study aimed to develop and validate a robust machine learning (ML) framework to forecast perinatal complications and facilitate early risk identification.</p> Methods <p>Perinatal mortality, preterm birth, low birth weight and macrosomia are defined as adverse outcomes. Analyzing a retrospective cohort of 5,710 pregnant women, we identified 22 initial variables using Lasso regression integrated with seven ML-based screening algorithms. Subsequently, eight predictive models were constructed and benchmarked via internal and external validation. Model performance was rigorously evaluated using receiver operating characteristic (ROC), precision‑recall (PR), calibration, and decision curves.</p> Results <p>Seven key predictors were identified, including gestational hypertension, obstetric history, and hepatic markers (Albumin, Alanine Aminotransferase, Globulin). The XGBoost model consistently outperformed its counterparts, demonstrating superior discriminative power (area under the curve (AUC) and F1-score) and clinical utility, as confirmed by the DeLong test and Kolmogorov‑Smirnov (KS) statistics. Based on XGBoost probabilities, we established a three-tier risk stratification: low-risk (&lt; 0.28), medium-risk (0.28–0.52), and high-risk (≥ 0.53).</p> Conclusions <p>Our ML-based framework offers a reliable tool for early risk assessment in gestational anemia, enabling clinicians to implement individualized management strategies through precise risk stratification.</p>

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Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Yayang Duan,
  • Fang He,
  • Ming Ge,
  • Haonan Zhang,
  • Baozhong Hu,
  • Chuanfen Gao,
  • Xianyue Yang,
  • Chaoxue Zhang,
  • Yi Zhou

摘要

Background

Gestational anemia significantly elevates the risk of adverse maternal and neonatal outcomes, necessitating early predictive tools for targeted intervention. This study aimed to develop and validate a robust machine learning (ML) framework to forecast perinatal complications and facilitate early risk identification.

Methods

Perinatal mortality, preterm birth, low birth weight and macrosomia are defined as adverse outcomes. Analyzing a retrospective cohort of 5,710 pregnant women, we identified 22 initial variables using Lasso regression integrated with seven ML-based screening algorithms. Subsequently, eight predictive models were constructed and benchmarked via internal and external validation. Model performance was rigorously evaluated using receiver operating characteristic (ROC), precision‑recall (PR), calibration, and decision curves.

Results

Seven key predictors were identified, including gestational hypertension, obstetric history, and hepatic markers (Albumin, Alanine Aminotransferase, Globulin). The XGBoost model consistently outperformed its counterparts, demonstrating superior discriminative power (area under the curve (AUC) and F1-score) and clinical utility, as confirmed by the DeLong test and Kolmogorov‑Smirnov (KS) statistics. Based on XGBoost probabilities, we established a three-tier risk stratification: low-risk (< 0.28), medium-risk (0.28–0.52), and high-risk (≥ 0.53).

Conclusions

Our ML-based framework offers a reliable tool for early risk assessment in gestational anemia, enabling clinicians to implement individualized management strategies through precise risk stratification.