Predicting Low Birth Weight: The Power of Machine Learning

Babies born weighing less than 2.5 kg face a risk of death up to 20 times higher and are more prone to long-term health issues, including neurological disorders, cardiovascular diseases, diabetes, and growth delays. Researchers at the University of São Paulo (USP) have found that machine learning models can help identify these high-risk cases early, paving the way for timely, targeted interventions and reducing the likelihood of future complications (1^✔ ✔Trusted Source
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study

Go to source). The research, which represents the first application of advanced machine learning algorithms for this purpose in the country, was based on data from 1,579 pregnant women monitored by the Araraquara population cohort in the interior of the state of São Paulo, Brazil. Supported by FAPESP, the work also serves as a counterpoint to most studies of this type that use data from countries in the Global North.

The researchers tested four machine learning algorithms: Random Forest, XGBoost, LightGBM, and CatBoost. XGBoost was the most effective at identifying high-risk pregnancies.

AI and Machine Learning: Transforming Maternal and Child Health Outcomes

“The findings have a significant impact on clinical practice and public policy formulation, given that the use of artificial intelligence and machine learning can enable earlier interventions, helping to reduce the risks associated with low birth weight and improving maternal and child health,” says Patrícia Rondó, a professor at the University of São Paulo’s School of Public Health (FSP-USP) and the author of the study published in the journal BMC Pregnancy and Childbirth.

Low birth weight is a global health problem with links to medical factors, such as complications during pregnancy, as well as socioeconomic factors, including maternal age, education, and access to prenatal care.

According to the authors, the technology could enable healthcare professionals to implement early interventions, such as nutritional supplementation, maternal education, increased prenatal consultations, and counseling on lifestyle changes. These interventions could reduce the impact of the problem on newborns.

Rondó is also the coordinator of a population study conducted in Araraquara that assessed the nutritional status and body composition of 2,000 pregnant women and their children from the fetal stage onwards. In addition to serving as a basis for evaluating predictive algorithms for low birth weight, the sample, which is representative of the city of Araraquara and the surrounding region, has enabled a series of studies on obesity and genetic, environmental, and epigenetic factors associated with disease.

“The Araraquara cohort offers a unique opportunity by providing clinical, socioeconomic, behavioral, and environmental data on a population with characteristics that differ from those of populations in the Global North, where most studies of this type are conducted,” says Audêncio Victor, a data scientist and the lead author of the study. Victor is also a FAPESP fellow and the study is the subject of his doctoral research in epidemiology at USP, with a sandwich period at the London School of Hygiene and Tropical Medicine at London University.

Identifying Low Birth Weight Risk

Factors such as maternal age, anthropometric variables, socioeconomic status, and access to prenatal care were identified as key determinants of low birth weight. “The risk factors are well-known in the literature, and a predictive model such as the one we tested is important for screening higher-risk cases that deserve greater attention during prenatal care. In addition, these are simple, low-cost variables that are routinely collected in health services, which makes the model applicable even in regions with limited resources,” says Victor.

The researchers also found that the model based on data from Araraquara works for the population of southeastern Brazil, including the state of São Paulo, but there are limitations. “To apply the models in the Amazon or in African countries, for example, we’d need to make adjustments so that they’d become truly predictive. Each population has its own specific characteristics, and the models need to be calibrated so that they’re truly predictive in different geographical and social contexts,” the researcher adds.

Reference:

1. Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study - (https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-025-07351-3)

Source-Eurekalert