## 3 Result and Discussion
### SHAP Explanation  
According to the ML model performance matrices, it is clear that the XGB and GB models perform best. Therefore, we utilized the XGB model in our XAI analysis to interpret the outputs based on the influence of the input features. In Figure 10, the SHAP summary plot provides a detailed analysis of how various factors influence the probability of learning difficulties in children. Here, the negative SHAP value indicates the absence of learning difficulties, while the positive value indicates the presence of difficulties. The figure shows that removing privileges is a significant risk factor, increasing the probability of learning challenges. In contrast, a higher wealth index serves as a strong protective factor, reducing this risk by providing a more supportive  
Figure 8: Confusion metrics for various machine learning algorithms for child learning difficulty prediction. Figure 9: ROC AUC Curves for Various ML. Algorithms for Child learning difficulties prediction. environment for learning. Sex has a relatively smaller impact, suggesting that while gender differences exist, they are not as influential as socioeconomic factors. Negative behaviors, such as shaking the child, physical punishment (e.g., beating the child hard), and verbal abuse (e.g., calling the child dumb or lazy), are strongly associated with an increased likelihood of learning difficulties. These factors highlight the detrimental effects of harsh disciplinary actions on a child's cognitive development. On the other hand, higher levels of maternal education and better physical health indicators, such as a higher weight-for-age percentile and child's weight, act as protective factors, reducing the risk of learning difficulties. This emphasizes the importance of supportive environments in lowering the risk of learning difficulties and demonstrates how harmful negative behaviors and low socioeconomic status can be. Furthermore, the SHAP waterfall plots reveal how various factors contribute to predicting the learning skill difficulties for two specific children. Both plots emphasize the strong influence of parenting practices, physical health, and socio-economic factors on the likelihood of learning difficulties in children. In Figure 11, the model predicts a higher probability that the child has learning difficulties, as the output is f(x) = 1.229. The primary factors contributing to this prediction are the following:  
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* Beating hard (YES = 1) significantly increases the likelihood of learning difficulties (+0.51). Harsh physical punishment is strongly correlated with emotional and cognitive challenges in children (Beating or physically punishing a child is a crime punishable by law in many countries. Such abusive behavior has been proven to severely hinder a child's emotional, cognitive, and educational development, including their learning capabilities);
* Removal of privileges (YES = 1) adds positively to the prediction (+0.39), as this negative disciplinary action can affect a child's mental well-being and learning ability;
* Shaking the child (NO = 2) contributes negatively (\(-0.34\)), implying that the absence of shaking reduces the likelihood of learning difficulties;
* Sex (MALE = 1) increases the likelihood of difficulties (+0.2), indicating that male children may face a higher probability of learning issues in this dataset;
* Mother's education (Primary = 1) has a moderate positive influence (+0.18), showing that lower maternal education correlates with a higher risk of learning difficulties;  
Figure 10: SHAP summary plot showing the influence of input features on children learning difficulties.