Machine learning for predicting the number of children among maternal health in Inimutaba – Minas Gerais - Aquino, Aurélio; Cerri, Ricardo (Brazilian Journal of Biometrics, 2026)
This study describes the profile of low-income mothers who already had children and received prenatal care in the municipality of Inimutaba, Minas Gerais, between January 2020 and December 2023. Sociodemographic variables were analyzed to identify the factors most strongly associated with the number of children per woman. The sample included 134 women with an average age of 29 years, the majority of whom self-identified as 86.56%, living on a minimum wage 94%, and having completed high school 94%. To investigate the determinants of fertility within this population, we applied an Ordinary Least Squares (OLS) regression and a Classification and Regression Tree (CART) algorithm. Although the in-sample OLS model initially exhibited a high coefficient of determination approx 0.91%, internal validation through 10-fold cross-validation demonstrated substantially lower generalization performance approx 0.37%, indicating overfitting. The most relevant predictors after model refinement were family size, household income, marital status (single), and age. The CART model showed similar patterns. The fully grown tree overfitted the training data, whereas cost-complexity pruning with an optimal parameter of 0.113% improved test accuracy to 74%. Despite this improvement, the model exhibited limited discriminative ability (AUC = 0.55), reinforcing the importance of careful interpretation in small epidemiological datasets. Overall, the findings indicate that fertility patterns among low-income mothers in Inimutaba are closely linked to socioeconomic vulnerability. Machine learning and statistical models, when combined with rigorous internal validation, can help identify high-risk subgroups and support targeted interventions for maternal health.