## 5 Policy Implications  
Figure 15 illustrates a structured framework that outlines a data-driven approach for predicting and addressing early childhood learning difficulties. It integrates family data collected by NGOs or health organizations, focusing on health, nutrition, family guidance, education, and socioeconomic status. Data were entered, processed, and analyzed through ML models to make predictions. XAI techniques were applied to identify key factors influencing learning difficulties, enhancing the interpretability of results. The identified learning gaps inform targeted policy implications and interventions, such as early child \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Findings** & **Data Source** & **ML Application** & **Statistical Analysis** & **XAI Uses** & **ML Performance** \\ \hline \multirow{4}{*}{[8]} & Identified factors & Data from & & & & & \\  & impacting & 6514 students over & & & & \\  & academic & 11 years at a & & & & & \\  & performance using & public university & & & & \\  & ML algorithms & in Oman & & & & \\ \hline \multirow{4}{*}{[9]} & Identified risk & 2018–2019 NSCH & Logistic & & & & RF classifier \\  & factors for ADHD & data from & regression and & & & & achieved \\  & in children using & 45,779 children & eight ML & & & & 85.5\% accuracy, \\  & ML classification & aged 3–17 years & classifiers & & & & sensitivity of \\  & & & & & & 84.4\%, specificity \\  & & & & & & of 86.4\%, and \\  & & & & & & AUC of 0.94 \\ \hline \multirow{4}{*}{[10]} & Predicted & Australian child & & & & & RF model \\  & depression in & and adolescent & & & & & achieved \\  & children and & responses from & & & & & 95\% accuracy \\  & adolescents using & 6310 participants & & & & & and \\  & ML models & (2013–2014) & & & & & 99\% precision \\ \hline \multirow{4}{*}{[11]} & Evaluated ML & & & & & & LR model \\  & techniques for & 5084 students & & & & & achieved \\  & academic & aged 10–17 in the & & & & & sensitivity of \\  & performance & West Bank & & & & 94.3\%, specificity \\  & factors & & & & & & of 96.3\%, and \\  & & & & & & AUC of 99.3\% \\ \hline \multirow{4}{*}{[15]} & Improved early & Obtained from & & & & & ANN achieved \\  & ASD diagnosis & UCI Repository; & & & & & 94.24\% accuracy, \\  & using machine & 292 children and & RF, KNN, XGB, & & & & precision 89.80\% \\  & learning & 704 adults & RF, KNN, XGB, & & & & recall 92\%, \\  & & & & & & & F1-score, 91\%, \\  & learning & 704 adults & & & & & specificity 95.05\%, \\  & & & & & & & and AUC of 93.84\% \\ \hline \multirow{4}{*}{[16]} & Enhanced toddler & From UCI ML & & & & & LR achieved \\  & ASD diagnosis & Repository; & & & & & 100\% accuracy, \\  & using machine & 1054 toddlers & DT, KNN, and & & & & 100\% precision, \\  & using machine & (12–36 months) via & NB & & & & 100\% recall, and \\  & learning. & ASDTests app & & & & & 100\% F1-score \\ \hline \multirow{4}{*}{This work} & Identified key & & & & & & XGB classifier \\  & factors and & & & & & achieved \\  & predicted learning & Refined dataset of & & & & 95\% accuracy, \\  & difficulties in & 13,274 samples & algorithms, and & & & 97\% precision, \\  & Bangladesh & and 19 features & SHAP and & & & & 93\% recall, \\  & children under & from MICS 2019 & LIME & & & & 95\% F1-score, \\  & 5 years old using & & & explanations & & & 93\% NPV, and \\  & ML models & & & & & 95\% ROC \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of research outcomes. hood education, health services, parenting support, nutritional programs, and economic assistance. This framework provides actionable insights to address disparities and improve educational outcomes through informed policymaking. The following steps will help in policy implications for addressing child learning disabilities:  
* **Health and Nutrition Interventions:** To support children's health and development, it is essential to provide nutritional supplements or balanced meals to promote proper growth.