Determinants and severity classification of singleton low birth weight infants in India: a hybrid statistical and machine learning analysis

Low birth weight (LBW) is a critical indicator of neonatal health that strongly determines morbidity and long-term developmental issues. India bears a disproportionately high burden of LBW, especially among socio-economically disadvantaged populations. Early detection of LBW risk factors enables timely and effective interventions to minimize the burden. The study therefore seeks to introduce a novel combination of categorical data transformation and machine-learning (ML)-based predictive modeling on NFHS-5 data for newborns at risk of LBW in India. Based on available data from over 175,000 singleton births, exposure variables were grouped into four domains: socio-economic, prenatal, maternal morbidity, and maternal nutritional factors. CATPCA revealed moderate-to-high component loadings for maternal morbidity, prenatal care, and nutrition, while ANN modeling identified the same determinants as the strongest factors, with socio-economic factors and dietary irregularities being less influential. The ANN achieved 94.6% accuracy, representing strong predictive performance for identifying at-risk newborns.