The project emerges as an opportunity to explore new construction techniques and materials, reinforcing its experimental profile and total creative freedom. With the façade facing the lush greenery of the avenue, the residence harmoniously integrates into the environment, reflecting a design that prioritizes both functionality and connection with nature. © Rafa D'AndreaAnother highlight of the project is the natural ventilation system provided by the lantern a tubular steel structure located between the technical floor and the upper floor This ingenious solution utilizes convection to keep the house ventilated and comfortable while protecting the interior of the residence from rainwater ingress You'll now receive updates based on what you follow Personalize your stream and start following your favorite authors If you have done all of this and still can't find the email Metrics details 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 We analyzed data from 1,579 pregnant women enrolled in the Araraquara Cohort Predictor variables included maternal sociodemographic were trained using an 80/20 train-test split and 10-fold cross-validation 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) Variable importance was evaluated using Shapley values Maternal gestational age was the most influential predictor followed by marital status and prenatal care frequency Shapley analysis provided interpretable insights into variable contributions supporting the clinical applicability of the models 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 The application of these algorithms can enhance the precision and reliability of predictions contributing to early prevention and intervention strategies for fetal growth issues The aim of this study is to develop a predictive model for LBW in pregnant women using machine learning algorithms By incorporating advanced techniques such as SMOTE and Shapley values we aim to improve the accuracy and interpretability of predictions thereby enabling more effective strategies to address fetal growth problems early and improve neonatal outcomes The sample included women with a gestational age of ≤ 19 weeks who received prenatal care at Basic Health Units in Araraquara The pregnant women were followed quarterly throughout their pregnancy until the birth of their children between 2017 and 2022 Women with twin pregnancies and those who experienced miscarriage were excluded only data from the pregnancy were considered The data used in this study were accessed between May 2024 and July 2024 The outcome of low birth weight was analyzed based on the dichotomous classification of birth weight, defined as low birth weight: < 2500 g and normal weight: ≥ 2500 g. The predictor variables are illustrated in Table 1 The study was approved by the Research Ethics Committee with Human Subjects at the School of Public Health under protocol number CAEE: 59787216.2.0000.5421 All participants signed the Informed Consent Form before participating The participants were informed about the objectives of the study ensuring that their participation was entirely voluntary All participants provided informed consent consistent with the principles outlined in the Helsinki Declaration An example of the workflow diagram for classifying birth weight adequacy The data analysis was conducted using Python (version 3.9) with key libraries including pandas (1.3.5) and numpy (1.21.4) for data preprocessing the official libraries for CatBoost (1.0.4) and LightGBM (3.3.2) were used for these respective algorithms According to Table 2 the maternal characteristics of 1,579 pregnant women from the Araraquara cohort were evaluated The women had an average age of 28.4 years a pre-pregnancy body mass index (BMI) of 24.7 kg/m² Most women (88.4%) had an education level equal to or greater than 8 years and 87.7% were married or in a stable relationship with most having a family income of R$563 and being non-smokers A total of 11.3% of the women had a urinary tract infection during pregnancy CRP (C-reactive protein) levels were 3.3 mg/L (interquartile range: 1.4–7.8) and HOMA (homeostasis model assessment) values were 2.9 units (interquartile range: 1.3–6.1) shows the distribution of low birth weight in the Araraquara cohort where 1,309 (91.2%) had normal birth weight we evaluated the predictive capacity of various ML models The analysis focused on multiple performance metrics such as AUROC and the MCC to provide a comprehensive evaluation of the ability of these models to classify cases of low birth weight Performance by ROC curve of ML (Random Forest Predictors of LBW (Shapley Variable Importance) for the best model - XGBoost Figure 3 shows the importance of the predictors for LBW using Shapley values for the best-performing model The most important variable identified was gestational age standing out as the factor with the most influence on predicting low birth weight maternal marital status and the absence of regular physical activity during pregnancy were significant predictors Other factors contributing substantially included maternal race underscoring the importance of socioeconomic and behavioral variables Variables such as smoking and alcohol consumption during pregnancy appeared with less relevance compared to the previously mentioned factors illustrates the strength of variable contributions to the prediction of LBW using Shapley values in the XGBoost model provide a detailed view of how each factor contributes to increasing or reducing the risk of LBW The use of ML in LBW prediction is gaining traction as these models demonstrate significant potential across various clinical and public health applications The use of Shapley values in our study provided additional insights into the interpretability of the models allowing for a more granular understanding of how each variable contributes to the prediction of LBW This aspect is particularly important in clinical applications where transparency and explainability of the models are crucial for their adoption by healthcare professionals the Shapley analysis in our study indicated that gestational weight gain had the strongest influence on LBW predictions followed by maternal race and prenatal care visits These findings mirror global trends in LBW prediction and prenatal care are recognized as key determinants of birth outcomes Their study found that socioeconomic factors including maternal education and access to prenatal care significantly contributed to the risk of LBW similar to our results from the Araraquara cohort Both studies emphasize the importance of early identification of high-risk pregnancies through predictive models especially in resource-limited settings where timely interventions can significantly improve neonatal outcomes While the results of this study are promising the cohort used in this study was from a specific region in Brazil which may limit the generalizability of the findings to other populations Future research should aim to validate these models across different regions and populations to ensure their broader applicability while our models demonstrated high predictive accuracy further research is needed to assess their integration into clinical workflows and their potential impact on perinatal care future studies could explore the use of these models in combination with mobile health (mHealth) technologies to improve prenatal care in low-resource settings although we employed robust techniques such as SMOTE to handle class imbalance and Shapley values to interpret model predictions the models still require validation in real-world clinical environments The practical deployment of ML models in healthcare settings involves challenges related to data privacy which should be thoroughly addressed before these models can be widely adopted This study successfully developed and evaluated machine learning models for predicting low birth weight in neonates The XGBoost model demonstrated the highest predictive performance with excellent discrimination between neonates at risk of LBW and those with normal birth weight The application of these models in clinical practice has the potential to improve early detection of high-risk pregnancies enabling timely and personalized interventions that could significantly improve neonatal outcomes Given the increasing global focus on maternal and neonatal health these findings hold important implications for both clinical practice and public health policy The integration of machine learning models into prenatal care systems could offer a transformative approach to preventing adverse birth outcomes particularly in low-resource settings where LBW remains a critical challenge The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The code developed for constructing the algorithms along, is available on Github (https://github.com/Audency/Predictors-of-Low-Birth-Weight-using-Machine-Laerning-.git.) Risk factors for low birth weight in hospitals of North Wello zone Evaluating association of maternal nutritional status with neonatal birth weight in term pregnancies: A Cross-Sectional study with unexpected outcomes Revista Brasileira De Cineantropometria E Desempenho Humano O Crescimento e desenvolvimento Frente à prematuridade e Baixo peso Ao nascer Low birth weight and its associated factors The limits of small-for-gestational-age as a high-risk category Gätjens I, Fedde S, Schmidt SCE, Hasler M, Plachta-Danielzik S, Müller MJ, et al. Relationship between birth weight, early growth rate and body composition in 5 to 7-year-old children. Obes Facts. 2022. https://doi.org/10.1159/000522509 Perinatal health predictors using artificial intelligence: A review A systematic review of the digital interventions for fighting COVID-19: the Bangladesh perspective Machine learning in ‘big data’: handle with care Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes Building a predictive model of low birth weight in low- and middle-income countries: a prospective cohort study Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country SMOTE: synthetic minority Over-sampling technique Machine learning-based approach for predicting low birth weight Issue of data imbalance on low birthweight baby outcomes prediction and associated risk factors identification: establishment of benchmarking key machine learning models with data rebalancing strategies The impact of gestational weight gain on fetal and neonatal outcomes: the Araraquara cohort study Predictive modeling of gestational weight gain: a machine learning multiclass classification study CatBoost: unbiased boosting with categorical features In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining Lightgbm: A highly efficient gradient boosting decision tree Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures In: International conference on machine learning Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes Machine learning for fetal growth prediction Machine learning methods for preterm birth prediction: A review Machine learning to predict pregnancy outcomes: a systematic review synthesizing framework and future research agenda Deep neural networks and tabular data: A survey Tabular data: deep learning is not all you need When Do Neural Nets Outperform Boosted Trees on Tabular Data Predicting risks of low birth weight in Bangladesh with machine learning Download references The authors gratefully acknowledge the professionals and graduate students who collaborated in the data collection for the Araraquara cohort This study was supported by the São Paulo Research Foundation (FAPESP)(grant number 2015/03333–6) Audêncio Victor has received a scholarship from São Paulo Research Foundation (FAPESP) (grant number 2023/07936-3) Faculdade de Saúde Pública- USP Avenida Doutor Arnaldo Administração e Contabilidade de Ribeirão Preto SPX: visualization and writing - original draft All authors contributed to the article and approved the submitted version The research received ethical approval from the Research Ethics Committee with Human Subjects at the School of Public Health before the commencement of data collection as per protocol CAEE: 59787216.2.0000.5421 and opinion number 1.885.874 The authors declare no competing interests Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Below is the link to the electronic supplementary material Download citation DOI: https://doi.org/10.1186/s12884-025-07351-3 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. RIO DE JANEIRO (LNB) - Flamengo are in a rich vein of form heading into the Liga Sudamericana.\r\n\r\nThe competition tips off on February 17 and Flamengo, who lost out to Universo/BRB in their battle to reach the FIBA Americas League Final Four, have rebounded to win all four of their top flight games in Brazil.\r\n\r\nIn Sao Paulo, meanwhile, Lupo/Araraquara ... HomeNewsBRA – Flamengo flying in Brazil but Araraquara upset Universo/BRB FIBA BasketballBRA – Flamengo flying in Brazil but Araraquara upset Universo/BRB RIO DE JANEIRO (LNB) - Flamengo are in a rich vein of form heading into the Liga Sudamericana The competition tips off on February 17 and Flamengo who lost out to Universo/BRB in their battle to reach the FIBA Americas League Final Four have rebounded to win all four of their top flight games in Brazil Connecting decision makers to a dynamic network of information Bloomberg quickly and accurately delivers business and financial information EMTs arrive with a patient at Vila Xavier Hospital in Araraquara The city has seen more Covid-19 deaths in the first two months of the year than in all of 2020.  Three adjacent cities with three different approaches illustrate the despair Parece que a página que você está procurando não está disponível. Connecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world 2015 at 9:08 AM ESTBookmarkSaveLock This article is for subscribers only The tang of Jose Luis Cutrale’s orange juice factory permeates the air in the Brazilian town of Araraquara For visitors who make the 173-mile trek northwest from Sao Paulo the aroma is a welcome respite from the stench that normally emanates from the city’s sewage-choked Tiete river the Sucocitrico Cutrale Ltda plant reminds them of home This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.