## 3 Result and Discussion
### ML Model Performance  
These models were chosen to effectively capture the complex and multi-faceted nature of the dataset, which includes various socio-economic, health, and educational indicators relevant to learning difficulties. KNN was used for its simplicity and effectiveness in handling non-linear relationships within the dataset. LRC was chosen for its interpretability and ability to model linear relationships. DT and RF were selected for their flexibility in managing hierarchical data, while GB and XGB were employed to improve predictive accuracy through their iterative error-correcting process and capacity to handle complex interactions in the data. By utilizing these models, the study aimed to validate its findings across different ML algorithms, ensuring the robustness of the results within the context of the MICS 2019 dataset. Figure 7 presents a comprehensive comparison of several machine learning algorithms applied to the MICS 2019 dataset for predicting child learning abilities. We evaluated the models using various performance metrics and assessed them through 10-fold cross-validation. Furthermore, we analyzed the performance metrics of various classification models to assess their effectiveness in predicting child learning difficulties. The DT model demonstrated moderate performance, with an accuracy of 0.70, precision of 0.67, and recall of 0.80, resulting in an F1-score of 0.73. In contrast, the KNN model showed better results with an accuracy of 0.86, a high recall of 0.97, and an F1-score of 0.87. LRC performed  
Figure 6: Correlation between child’s weight, weight-for-age percentile, and learning skill compared to others. similarly across most metrics, with values around 0.69. The RF model performed well, achieving an accuracy of 0.91, precision of 0.89, recall of 0.93, and an F1-score of 0.91. GB and XGB yielded the best results, achieving exceptional performance metrics such as accuracy of 0.95, precision of 0.96-0.97, recall of 0.93, and an F1-score of 0.95. These models also had high NPV and ROC AUC scores of 0.93 and 0.95, respectively. This analysis suggests that GB and XGB are the most effective models for this application, outperforming other classifiers in nearly all metrics. The results show how well different ML models predict learning difficulties in children. It compares their performance using various measures that assess how accurately the models identify children with and without difficulties and how balanced their predictions are. RF, XGB, KNN, and GB perform very well, with results close to perfect, meaning they make highly reliable predictions and are good at correctly identifying both children with and without learning difficulties. In contrast, models like DT and LRC perform slightly less effectively. Their predictions are still useful but less consistent and accurate compared to the top-performing models. The ML models in this study were trained on a workstation equipped with an Intel Core i3 CPU @ 2.3 GHz and 12 GB of RAM. Training times varied across models, reflecting their complexity and the chosen hyperparameters. The Table 6 shows that the DT model was the fastest, with a training time of 0.06 s and a testing time of 0.0007 s. Following DT, LRC had the best testing time at 0.0005 s, and XGB came next with a testing time of 0.0094 s. Other algorithms, while requiring more time, still provided valuable results with good accuracy and performance. These training times demonstrate the computational efficiency of each model within the constraints of the available resources, allowing for effective model evaluation and tuning without significant delays. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Train\_Time (s)** & **Test\_Time (s)** \\ \hline DT & 0.063787 & 0.000712 \\ \hline KNN & 2.099552 & 0.037495 \\ \hline LRC & 0.243012 & 0.00052 \\ \hline RF & 2.256923 & 0.261399 \\ \hline GB & 5.796678 & 0.011094 \\ \hline XGB & 1.841549 & 0.009374 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time Requirements for Training ML Models.