A SHAP-Based Deep Learning Framework for Low Birth Weight Prediction: Balancing, Explainable AI, and Individualized Risk Intervention (Preprint)

BACKGROUND Low birth weight (LBW), defined as less than 2,500 grams at birth, is a persistent public health issue associated with increased risks of neonatal mortality, long-term developmental challenges, and chronic diseases. Alabama consistently reports higher-than-average LBW rates, emphasizing the need for effective early prediction and intervention strategies. OBJECTIVE This study aimed to develop an explainable machine learning (ML) framework to predict LBW using large-scale predelivery data, address class imbalance with multiple data-balancing techniques, and generate personalized risk assessments and intervention strategies. METHODS We used 514,294 Alabama birth records from 2014 to 2022, with 165 pre-delivery features. Seven balancing techniques were applied: Random OverSampling, Random UnderSampling, SMOTE, ADASYN, Tomek Links, SMOTE-Tomek, and class weight adjustment. Seven ML models were evaluated: Logistic Regression (LR), K-Nearest Neighbors (k-NN), Random Forest (RF), AdaBoost, XGBoost, and Multilayer Perceptron (MLP). Bayesian hyperparameter optimization and stratified train-test splits were used. SHAP (Shapley Additive Explanations) provided global and individual feature importance, and counterfactual sensitivity analysis simulated risk interventions. RESULTS The MLP model with Random UnderSampling achieved the best performance (recall = 0.7779). Key predictors included prenatal visits, pregnancy weight gain, pre-pregnancy weight, and gestational hypertension. Socioeconomic and behavioral variables (e.g., smoking, education, marital status), along with racial disparities, also contributed significantly to LBW risk. Individual-level SHAP and counterfactual analysis demonstrated how modifiable risk factors could lower LBW probabilities. CONCLUSIONS This framework combines predictive accuracy, interpretability, and personalization, offering a valuable tool for early, equitable LBW risk identification and intervention.