# Predicting Fetal Birthweight from High Dimensional Data using Advanced Machine Learning

arXiv: https://arxiv.org/abs/2502.14270 · HTML: https://mlsanity.com/paper/2502.14270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Hopular: Modern Hopfield Networks for Tabular Data](https://mlsanity.com/paper/2206.00664/) (2022)
- [Feature Selection via Mutual Information: New Theoretical Insights](https://mlsanity.com/paper/1907.07384/) (2019)
- [Variable Selection Using Bayesian Additive Regression Trees](https://mlsanity.com/paper/2112.13998/) (2021)
- [On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets](https://mlsanity.com/paper/2403.14687/) (2024)
- [Kendall transformation: a robust representation of continuous data for information theory](https://mlsanity.com/paper/2006.15991/) (2020)

## Abstract (arXiv)

Birth weight serves as a fundamental indicator of neonatal health, closely linked to both early medical interventions and long-term developmental risks. Traditional predictive models, often constrained by limited feature selection and incomplete datasets, struggle to achieve overlooking complex maternal and fetal interactions in diverse clinical settings. This research explores machine learning to address these limitations, utilizing a structured methodology that integrates advanced imputation strategies, supervised feature selection techniques, and predictive modeling. Given the constraints of the dataset, the research strengthens the role of data preprocessing in improving the model performance. Among the various methodologies explored, tree-based feature selection methods demonstrated superior capability in identifying the most relevant predictors, while ensemble-based regression models proved highly effective in capturing non-linear relationships and complex maternal-fetal interactions within the data. Beyond model performance, the study highlights the clinical significance of key physiological determinants, offering insights into maternal and fetal health factors that influence birth weight, offering insights that extend over statistical modeling. By bridging computational intelligence with perinatal research, this work underscores the transformative role of machine learning in enhancing predictive accuracy, refining risk assessment and informing data-driven decision-making in maternal and neonatal care. Keywords: Birth weight prediction, maternal-fetal health, MICE, BART, Gradient Boosting, neonatal outcomes, Clinipredictive.