Abstract
Small for gestational age (SGA) is a significant concern in obstetrics, with implications for stillbirth, neonatal mortality, and long-term health outcomes. Early detection of SGA is crucial for prevention and treatment, but current methods have limitations. This study aimed to develop an artificial intelligence (AI)-based algorithm to predict SGA using sociodemographic and obstetric features during pregnancy. A total of 102 pregnant women meeting specific criteria were included in the study. The feature impact factors considered important factors for predicting SGA at birth were maternal weight, length, age, gravida, and parity. The LGBM model demonstrated the highest accuracy rate (71.4%) and AUC-ROC (62.7%) in predicting SGA, showcasing its potential for improving the prediction and treatment of SGA pregnancies. The study highlights the importance of using AI-driven methods in obstetrics to improve decision-making and patient care in high-risk pregnancy scenarios. Although AI/ML techniques have shown promise in enhancing the screening for SGA, further refinement and validation of algorithms are necessary before clinical implementation. Consistency in diagnostic criteria and quality assessment is essential for the widespread adoption of these methods in clinical settings.