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Background Early marriage and socioeconomic factors, which expose young mothers to early pregnancy under situations of adversity, are, as a result, dramatically associated with increased risk of children’s morbidity and perpetuate intergenerational cycles of poor health and disparity. Thus, this study aims to assess the association between early marriage and socioeconomic factors on children’s morbidity in South Asian countries using national survey data. Materials and methods This study utilized the most recent nationally representative Demographic and Health Survey (DHS) child datasets from five South Asian countries—Bangladesh, India, Pakistan, Afghanistan, and Nepal—comprising a total sample of 286,131 children. The study’s outcome variable was the child morbidity. In addition to descriptive statistics, a two-stage binary logistic regression was used to analyze factors influencing child morbidity. Results In South Asia, Pakistan had the highest prevalence of child morbidity at 44.38%, followed by Afghanistan at 42.25%, Bangladesh at 34.20%, Nepal at 28.33%, and India with the lowest at 17.72%. Binary logistic regression revealed key factors associated with under-five morbidity in South Asia. Children born to early-married mothers in Pakistan had a significantly higher risk of morbidity (OR = 1.43, 95% CI: 1.20–1.70). Higher morbidity was also associated with maternal secondary education in Pakistan (OR = 1.88, 95% CI: 1.40–2.53), eight or more antenatal care visits in Pakistan (OR = 1.75, 95% CI: 1.17–2.63), Afghanistan (OR = 2.23, 95% CI: 1.83–2.71), and female-headed households in India and Pakistan (OR = 1.14, 95% CI: 1.05–1.24). Breastfeeding was connected to higher child morbidity in Bangladesh, India, Pakistan, and Afghanistan. In contrast, higher maternal education was associated with a significant reduction in child morbidity in both India (OR = 0.81, 95% CI: 0.70–0.93) and Afghanistan (OR = 0.57, 95% CI: 0.45–0.72). Rural residence (OR = 0.80, 95% CI: 0.73–0.87) in Afghanistan, as well as wealth status in India (OR = 0.79, 95% CI: 0.72–0.87), were protective factors. Conclusion These findings highlight the urgent need to delay early marriage and address socioeconomic disparities to reduce child morbidity in South Asia. Improving maternal education, enforcing laws to delay the age at marriage, and increasing access to healthcare are crucial for enhancing child health and well-being in the region.

Low birth weight (LBW) remains a leading cause of neonatal mortality and long-term morbidity in low- and middle-income countries. This study aimed to develop and evaluate machine learning classifiers for predicting LBW using nationally representative survey data from Bangladesh, while explicitly distinguishing predictive modeling from causal inference. We analyzed data from the 2022 Bangladesh Demographic and Health Survey (BDHS), yielding a final analytic sample of 3,400 mother-child pairs after complete-case exclusion. Survey weights, stratification, and clustering were incorporated into all modeling steps via sample_weight parameters and cluster-aware data splitting. Class imbalance was addressed using native class-weight optimization (scale_pos_weight, class_weight = \"balanced\") rather than synthetic oversampling to preserve survey representativeness. Seven machine learning classifiers were evaluated under a cluster-aware train-validation-test split. Model performance was assessed using discrimination metrics (AUROC, PR-AUC), calibration metrics (Brier score, slope, intercept), and 95% confidence intervals derived via stratified bootstrap resampling (B = 1,000). SHapley Additive exPlanations (SHAP) were used for model interpretability, with explicit framing of findings within a predictive context. XGBoost demonstrated the best calibrated and discriminative performance on the independent test set: AUROC = 0.828 (95% CI: 0.764-0.887), sensitivity = 0.711 (0.600-0.816), specificity = 0.847 (0.814-0.876), Brier score = 0.095 (0.077-0.114). SHAP analysis identified geographical division, birth order, paternal education, and household wealth as the most influential predictors. Variables non-significant in bivariate analysis but influential in XGBoost (e.g., child's sex, maternal age) likely contribute through higher-order interactions captured by tree-based ensembles. The positive association between antenatal care visits and predicted LBW risk likely reflects clinical triage patterns rather than causal harm. Survey-aware machine learning, particularly XGBoost, provides a robust framework for LBW risk stratification in Bangladesh. While observational design precludes causal inference and external validation remains necessary, these findings support the potential utility of interpretable ML models for informing targeted maternal health interventions. Future work should prioritize prospective validation and incorporation of clinical biomarkers.

Oral squamous cell carcinoma (OSCC) represents a significant global health concern, with increasing incidence rates and challenges in early diagnosis and treatment planning. Early detection is crucial for improving patient outcomes and survival rates. Deep learning, a subset of machine learning, has shown remarkable progress in extracting and analyzing crucial information from medical imaging data.EfficientNetB3, an advanced convolutional neural network architecture, has emerged as a leading model for image classification tasks, including medical imaging. Its superior performance, characterized by high accuracy, precision, and recall, makes it particularly promising for OSCC detection and diagnosis. EfficientNetB3 achieved an accuracy of 0.9833, precision of 0.9782, and recall of 0.9782 in our analysis. By leveraging EfficientNetB3 and other deep learning technologies, clinicians can potentially improve the accuracy and efficiency of OSCC diagnosis, leading to more timely interventions and better patient outcomes. This article also discusses the role of deep learning in advancing precision medicine for OSCC and provides insights into prospects and challenges in leveraging this technology for enhanced cancer care.