Preterm low birth weight (PLBW) is a major global contributor to neonatal morbidity and mortality. This study aims to develop and evaluate a novel artificial neural network (ANN) based predictive model that integrates hematological, periodontal, obstetric, and behavioral parameters to estimate the risk of PLBW with high accuracy. A prospective case control study was conducted using data from 100 pregnant women, comprising 26 input parameters from diverse domains such as hematology (mean platelet volume MPV, neutrophil to lymphocyte ratio NLR), periodontal status (Plaque Index PI, clinical attachment loss CAL), obstetric history, and lifestyle behaviors (smoking, oral hygiene). A multilayer perceptron ANN architecture optimized by the Levenberg–Marquardt algorithm was employed to train the model and assess its predictive performance. The developed ANN model showed acceptable predictive performance in estimating PLBW risk and performed better than the logistic regression model in this dataset. The model effectively captured complex, nonlinear interactions among the input variables, with a mean squared error (MSE) of 0.1022 and a correlation coefficient (R) of 0.776, indicating good model fit. This study presents an interdisciplinary and scalable AI driven framework that leverages hematological, dental and periodontal, and behavioral markers for early prediction of PLBW. The ANN model may provide a basis for future tools aimed at prenatal risk stratification, pending validation in larger, multicenter cohorts. • A novel artificial neural network (ANN) model was developed for early prediction of preterm and low birth weight (PLBW) risk. • The model integrates hematological (e.g., MPV, NLR), periodontal (e.g., PI, CAL, DMFT), and behavioral (e.g., smoking, oral hygiene) predictors. • Multilayer perceptron (MLP) architecture was optimized using the Levenberg–Marquardt algorithm to enhance prediction performance. • The ANN model demonstrated high accuracy (R = 0.776, MSE = 0.1022), outperforming traditional statistical approaches. • This interdisciplinary AI approach enables personalized prenatal risk stratification and supports preventive maternal care.
Temur et al. (Thu,) studied this question.