Exploring Childhood Disabilities in Fragile Families: Machine Learning Insights for Informed Policy Interventions

This study delves into the multifaceted challenges confronting children from vulnerable or fragile families, with a specific focus on learning disabilities, resilience (measured by grit), and material hardship—a factor intricately linked with children’s disabilities. Leveraging the predictive capabilities of machine learning (ML), our research aims to discern the determinants of these outcomes, thereby facilitating evidence-based policy formulation and targeted interventions for at-risk populations. The dataset underwent meticulous preprocessing, including the elimination of records with extensive missing values, the removal of features with minimal variance, and the imputation of medians for categorical data and means for numerical data. Advanced feature selection techniques, incorporating mutual information, the least absolute shrinkage and selection operator (LASSO), and treebased methods, were employed to refine the dataset and mitigate overfitting. Additionally, we addressed the challenge of class imbalance through the implementation of the Synthetic Minority Over-sampling Technique (SMOTE) to enhance model generalization. Various ML models, encompassing Random Forest, Neural Networks [multilayer perceptron (MLP)], Gradient-Boosted Trees (XGBoost), and a Stacking Ensemble Model, were evaluated on the Future of Families and Child Wellbeing Study (FFCWS) dataset, with fine-tuning facilitated by Bayesian optimization techniques. The experimental findings highlighted the superior predictive performance of Random Forest and XGBoost models in classifying material hardship, while the Stacking Ensemble Model emerged as the most effective predictor of grade point average (GPA) and grit. Our research underscores the critical importance of tailored policy interventions grounded in empirical evidence to address childhood disabilities within fragile families, thus offering invaluable insights for policymakers and practitioners alike.