Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study
Abstract Background Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost,...
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BMC
2025-03-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-025-07351-3 |
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| author | Audêncio Victor Francielly Almeida Sancho Pedro Xavier Patrícia H.C. Rondó |
| author_facet | Audêncio Victor Francielly Almeida Sancho Pedro Xavier Patrícia H.C. Rondó |
| author_sort | Audêncio Victor |
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| description | Abstract Background Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost, Catboost, and LightGBM. Methods We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort, a population-based longitudinal study. Predictor variables included maternal sociodemographic, clinical, and behavioral factors. Four ML algorithms Random Forest, XGBoost, CatBoost, and LightGBM, were trained using an 80/20 train-test split and 10-fold cross-validation. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Model performance was assessed using metrics such as area under the receiver operating characteristic curve (AUROC), F1-score, and precision-recall. Variable importance was evaluated using Shapley values. Results XGBoost demonstrated the best performance, achieving an AUROC of 0.94, followed by CatBoost (0.94), Random Forest (0.94), and LightGBM (0.94). Maternal gestational age was the most influential predictor, followed by marital status and prenatal care frequency. Behavioral factors, such as physical activity, also contributed to LBW risk. Shapley analysis provided interpretable insights into variable contributions, supporting the clinical applicability of the models. Conclusion Machine learning, combined with SMOTE, proved to be an effective approach for predicting LBW. XGBoost stood out as the most accurate model, but Catboost and Random Forest also provided solid results. These models can be applied to identify high-risk pregnancies, improving perinatal outcomes through early interventions. |
| format | Article |
| id | doaj-art-7e72413aece448f097246d301f4393b2 |
| institution | OA Journals |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-7e72413aece448f097246d301f4393b22025-08-20T01:48:11ZengBMCBMC Pregnancy and Childbirth1471-23932025-03-012511910.1186/s12884-025-07351-3Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort studyAudêncio Victor0Francielly Almeida1Sancho Pedro Xavier2Patrícia H.C. Rondó3School of Public Health, University of São Paulo (USP)Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto, FEA-RP/USPInstitute of Collective Health, Federal University of Mato Grosso. CuiabáSchool of Public Health, University of São Paulo (USP)Abstract Background Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost, Catboost, and LightGBM. Methods We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort, a population-based longitudinal study. Predictor variables included maternal sociodemographic, clinical, and behavioral factors. Four ML algorithms Random Forest, XGBoost, CatBoost, and LightGBM, were trained using an 80/20 train-test split and 10-fold cross-validation. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Model performance was assessed using metrics such as area under the receiver operating characteristic curve (AUROC), F1-score, and precision-recall. Variable importance was evaluated using Shapley values. Results XGBoost demonstrated the best performance, achieving an AUROC of 0.94, followed by CatBoost (0.94), Random Forest (0.94), and LightGBM (0.94). Maternal gestational age was the most influential predictor, followed by marital status and prenatal care frequency. Behavioral factors, such as physical activity, also contributed to LBW risk. Shapley analysis provided interpretable insights into variable contributions, supporting the clinical applicability of the models. Conclusion Machine learning, combined with SMOTE, proved to be an effective approach for predicting LBW. XGBoost stood out as the most accurate model, but Catboost and Random Forest also provided solid results. These models can be applied to identify high-risk pregnancies, improving perinatal outcomes through early interventions.https://doi.org/10.1186/s12884-025-07351-3Low birth weightMachine learningXGBoostRandom forestAraraquara cohort |
| spellingShingle | Audêncio Victor Francielly Almeida Sancho Pedro Xavier Patrícia H.C. Rondó Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study BMC Pregnancy and Childbirth Low birth weight Machine learning XGBoost Random forest Araraquara cohort |
| title | Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study |
| title_full | Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study |
| title_fullStr | Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study |
| title_full_unstemmed | Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study |
| title_short | Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study |
| title_sort | predicting low birth weight risks in pregnant women in brazil using machine learning algorithms data from the araraquara cohort study |
| topic | Low birth weight Machine learning XGBoost Random forest Araraquara cohort |
| url | https://doi.org/10.1186/s12884-025-07351-3 |
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