## 4 Comparison with Other Works  
This study compared its findings with research from Oman, Australia, and the U.S., focusing on early childhood learning outcomes. While previous studies used diverse datasets and methods, our approach uniquely applies the MICS 2019 dataset to the Bangladesh context. Unlike prior research, which often lacked explainability, we integrated SHAP and LIME to enhance model interpretability. This comparison highlights our study's contribution to understanding learning difficulties through a region-specific lens, offering actionable insights for policymaking and education strategies.  
Table 8 presents a detailed comparison of our work with other research studies, emphasizing methodologies, datasets, statistical applications of ML, and XAI techniques. The table highlights performance metrics like accuracy, precision, recall, and specificity to demonstrate the effectiveness of different approaches. Additionally, it outlines the integration of statistical ML and XAI tools, providing a comprehensive overview of how our work aligns with or advances existing research in the field.  
One study explored factors impacting academic performance using statistical ML techniques, specifically the J48 algorithm, applied to a dataset of 6514 students over 11 years at a public university in Oman. The study achieved an accuracy of 82.4% but did not integrate any XAI methods or statistical explanations to interpret the results [8]. Another study applied ML to identify risk factors for ADHD in children using data from 45,779 participants in the 2018-2019 National Survey of Children's Health (NSCH). While logistic regression and eight ML classifiers were tested, the RF classifier stood out with an accuracy of 85.5% and an AUC of 0.94. However, the study focused only on statistical ML performance without employing XAI to explain the models [9].  
Similarly, the study [10] predicted depression in children and adolescents using mental health and socio-environmental features from 6310 Australian participants (2013-2014). ML models like RF, XGB, DT, and Gaussian naive Bayes were used, with the RF model achieving 95% accuracy and 99% precision. Despite the robust performance, the study did not utilize XAI tools for model interpretability or statistical analysis of feature importance. Another research [11] evaluated ML techniques for predicting academic performance factors using data from 5084 students aged 10-17 in West Bank schools. Among the models tested (RF, ANN, SVM, DT, NB, and LR), LR achieved the best results with a sensitivity of 94.3%, specificity of 96.3%, and an AUC of 99.3%. However, no XAI techniques were used to explain the predictive factors.  
In studies focused on autism spectrum disorder (ASD), research [15] analyzed data from 292 children and 704 adults using multiple ML algorithms. The ANN model achieved 94.24% accuracy and a high specificity of 95.05%, but no XAI methods were applied to interpret the findings statistically. Another study [16] focused on toddler ASD diagnosis, achieving a perfect performance of 100% accuracy, precision, recall, and F1-score using LR but without incorporating statistical analysis or XAI tools.  
In contrast, our work integrated both statistical ML and XAI to identify key factors and predict learning difficulties in Bangladesh children under five years of age. Using a refined dataset from MICS 2019, we employed ML algorithms like DT, LR, KNN, RF, XGB, and GB, alongside SHAP and LIME for interpretability. This combination allowed us to explain the significance of individual features statistically while enhancing the transparency of our models. The XGB classifier demonstrated superior performance, achieving 95% accuracy, 97% precision, 93% recall, 95% F1-score, 93% NPV, and an ROC of 95%. Our work not only matches or exceeds the performance metrics of prior studies but also provides a deeper understanding of the underlying factors through the integration of XAI, setting it apart from existing research.