Predictive modeling with machine learning for low birth weight in the state of São Paulo: a retrospective population-based study.

BACKGROUND

Low birth weight (LBW) is a major public health concern associated with increased neonatal morbidity and mortality.

OBJECTIVE

This study was aimed to develop a predictive learning model using machine learning techniques to identify LBW from live birth data.

METHODS

A retrospective population-based study was conducted using data from the Brazilian Live Birth Information System (SINASC), which includes all live births registered in the state of São Paulo between 2019 and 2023. After data curation and exclusion of records with missing or unknown information, a total of 2,548,570 live births were analyzed. Predictors included maternal sociodemographic characteristics, obstetric history, prenatal care indicators, and newborn characteristics. Logistic regression and random forest models were trained using an 80/20 train-test split, with class imbalance addressed through class weighting. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).

RESULTS

The analysis showed that the balanced logistic regression model performed best in terms of sensitivity (0.688), F1-score (0.567), and AUC-ROC (0.850). A proportional analysis of the variables between the LBW and normal-weight groups revealed a higher prevalence of LBW among mothers with low education levels, without a partner, of Black or Brown ethnicity, aged ≤ 19 years or ≥ 35 years, with multiple pregnancies, with a reduced number of prenatal consultations, and with late initiation of prenatal care.

CONCLUSIONS

The findings highlight the importance of large-scale data analysis supported by machine learning techniques to inform public policies aimed at preventing LBW and promoting maternal and child health.