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Background <p>Prediction of Large for Gestational Age (LGA) risk is important as it can enable earlier, more effective interventions, and avoid or mitigate cumulative injury to both mother and baby at the time of delivery. The goal of this research is to improve the prediction of LGA using machine learning (ML) models and variables that are widely available as that allow for broad intervention in late pregnancy and during delivery.</p> Methods <p>An improved prediction of LGA was achieved using twelve ML models with hyperparameter optimization. Also, to improve the LGA prediction a data augmentation method was employed in the training set. Additionally, improvement in LGA prediction was obtained with four variable selection methods employed to identify the most significant variables. To rank the best models on the validation set after training, the Area under the Receiver Operating Characteristic Curve (AUROC) was used. Finally, to assess the generalization performance, the best models were evaluated on the test set.</p> Results <p>Our method enabled us to identify several models with high sensitivity and specificity. The best models included those that achieved a sensitivity of 0.84, a specificity of 0.84, an accuracy of 0.84, and AUCROC 0.83, requiring 14 variables. Another model reached an accuracy of 0.87, a sensitivity of 0.71, and a specificity of 0.90 with an AUCROC of 0.83 (14 variables). Additionally, a model with a sensitivity of 0.58, the same as that described by Hadlock et al. [1] for ultrasound, required 10 variables, and reached an accuracy of 0.91, a specificity of 0.97, and an AUCROC of 0.86. Both models included maternal BMI (body mass index), First Control, Maternal Weight, BMI Last Control, and EFW (estimated fetal weight) as the most important variables.</p> Conclusions <p>The main contributions of our study include the prediction of LGA using data obtained from standard clinical evaluations during prenatal care, as well as the development ML models, tuning the hyperparameters to improve prediction results, thus achieving high levels of sensitivity and specificity. To achieve optimal LGA prediction outcomes across different models, a data augmentation approach is introduced, an important improvement in LGA prediction over using only Hadlock’s ultrasound formula.</p>

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Machine learning to improve the prediction of Large for Gestational Age (LGA) neonates: a cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Gabriel Cubillos,
  • Max Monckeberg,
  • Alejandra Plaza,
  • Maria Morgan,
  • Pablo A. Estevez,
  • Mahesh Choolani,
  • Matthew W. Kemp,
  • Sarah W. L. Li,
  • Sebastian E. Illanes,
  • Claudio A. Perez

摘要

Background

Prediction of Large for Gestational Age (LGA) risk is important as it can enable earlier, more effective interventions, and avoid or mitigate cumulative injury to both mother and baby at the time of delivery. The goal of this research is to improve the prediction of LGA using machine learning (ML) models and variables that are widely available as that allow for broad intervention in late pregnancy and during delivery.

Methods

An improved prediction of LGA was achieved using twelve ML models with hyperparameter optimization. Also, to improve the LGA prediction a data augmentation method was employed in the training set. Additionally, improvement in LGA prediction was obtained with four variable selection methods employed to identify the most significant variables. To rank the best models on the validation set after training, the Area under the Receiver Operating Characteristic Curve (AUROC) was used. Finally, to assess the generalization performance, the best models were evaluated on the test set.

Results

Our method enabled us to identify several models with high sensitivity and specificity. The best models included those that achieved a sensitivity of 0.84, a specificity of 0.84, an accuracy of 0.84, and AUCROC 0.83, requiring 14 variables. Another model reached an accuracy of 0.87, a sensitivity of 0.71, and a specificity of 0.90 with an AUCROC of 0.83 (14 variables). Additionally, a model with a sensitivity of 0.58, the same as that described by Hadlock et al. [1] for ultrasound, required 10 variables, and reached an accuracy of 0.91, a specificity of 0.97, and an AUCROC of 0.86. Both models included maternal BMI (body mass index), First Control, Maternal Weight, BMI Last Control, and EFW (estimated fetal weight) as the most important variables.

Conclusions

The main contributions of our study include the prediction of LGA using data obtained from standard clinical evaluations during prenatal care, as well as the development ML models, tuning the hyperparameters to improve prediction results, thus achieving high levels of sensitivity and specificity. To achieve optimal LGA prediction outcomes across different models, a data augmentation approach is introduced, an important improvement in LGA prediction over using only Hadlock’s ultrasound formula.