## 3 Result and Discussion
### ML Model Performance
Figure 7: Performance metrics of various ML algorithms for predicting child learning difficulties. Furthermore, the Table 7 presents the performance metrics of the XGB model, evaluated using 1- and 10-fold cross-validation for predicting child learning difficulties. Each fold displayed consistent results, with high accuracy precision, recall, F1-score, NPV, and ROC AUC score. Overall, the table highlights the robust performance of the XGB model across multiple evaluation metrics. The confusion matrices in Figure 8 generated by the six different ML models DT, RF, LRC, KNN, GB, and XGB provide key insights into their effectiveness in predicting early child learning difficulties, where the outcomes are classified as either \"NO DIFFICULTY\" (0) or \"HAS DIFFICULTY\" (1). The XGB model stands out as a very good performer, with the highest number of true positives (1168) and true negatives (1208), coupled with the lowest number of false positives (36) and false negatives (79), indicating its high accuracy in identifying both children who have and those who do not have learning difficulties. Similarly, the GB model demonstrates strong performance, with slightly fewer true positives (1170), more true negatives (1212), and very few false positives (32), making it another reliable option. The RF model also performs well, with a favorable balance of true positives (1154) and true negatives (1100), though it has a moderate number of false positives (144) and false negatives (93), indicating some room for improvement in reducing misclassifications. The KNN model, despite its strong identification of true positives (1199), struggles with a higher number of false positives (324), making it less reliable in correctly identifying children without learning difficulties. The LRC, however, has a significant number of false negatives (397), posing a risk of under-detection. The DT model, with a substantial number of false positives (500), indicates a tendency to over-predict learning difficulties. Overall, XGB and GB emerge as the most effective models, offering the best balance between accurately predicting both classes and minimizing errors. The ROC AUC curves in Figure 9 present a comparative analysis of six ML models applied to a dataset on child learning difficulties. The XGB and GB models, indicated by the orange and purple curves, respectively, both achieved the highest ROC AUC of 0.95, demonstrating their exceptional ability to differentiate between classes. The RF model, represented by the red curve, also performed well with an ROC AUC of 0.91, though it lagged slightly behind XGB and GB. In contrast, the DT, LRC, and KNN models, represented by blue, cyan, and green curves, respectively, each achieved an ROC AUC of 0.70, 0.69, and 0.86. This suggests that these models are less effective compared to the top-performing XGB, GB, and RF models. Overall, the analysis indicates that XGB and GB are the most \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**K\_Fold** & **Accuracy** & **Precision** & **Recall** & **F1\_Score** & **npv** & **roc\_auc** \\ \hline
1\_Fold & 0.95 & 0.97 & 0.92 & 0.94 & 0.94 & 0.95 \\ \hline
2\_Fold & 0.94 & 0.95 & 0.92 & 0.94 & 0.94 & 0.94 \\ \hline
3\_Fold & 0.94 & 0.96 & 0.92 & 0.94 & 0.94 & 0.94 \\ \hline
4\_Fold & 0.95 & 0.97 & 0.93 & 0.95 & 0.94 & 0.95 \\ \hline
6\_Fold & 0.95 & 0.97 & 0.93 & 0.95 & 0.94 & 0.95 \\ \hline
7\_Fold & 0.95 & 0.97 & 0.93 & 0.95 & 0.94 & 0.95 \\ \hline
8\_Fold & 0.95 & 0.97 & 0.93 & 0.95 & 0.94 & 0.95 \\ \hline
9\_Fold & 0.95 & 0.97 & 0.93 & 0.95 & 0.94 & 0.95 \\ \hline
10\_Fold & 0.95 & 0.97 & 0.93 & 0.95 & 0.94 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: K-fold cross-validation performance metrics for XGB model predicting child learning difficulties. effective models for predicting child learning difficulties, with RF following closely behind, while DT, LRC, and KNN exhibit lower classification performance.