## 2 Materials and Methods
### Data Cleaning and Preprocessing  
The MICS 2019 dataset encompasses various clusters with different domains. Among these, we chose the children's health (ch) dataset because it supported all the necessary domains for determining the learning skills of children under five years old. Initially, the dataset contained 24,686 samples with 346 features covering various sociodemographic, health, and behavioral aspects related to children and their families. However, not all features were directly relevant to understanding early childhood learning difficulties. Initially, data cleaning and preprocessing were performed to exclude irrelevant variables, such as identifiers and redundant features, as well as features with a high percentage of missing values. Then, we selected all possible features that linked to early childhood learning outcomes, such as parenting behaviors, maternal education, and child health metrics. We also focused on features that were most relevant to the target variable to ensure the inclusion. After careful consideration, we identified 19 features that demonstrated statistical significance, predictive power, and alignment with domain-specific knowledge. This process involved evaluating the importance of each feature, checking Pearson correlations, and of the most significant ones for model accuracy [23; 24]. Subsequently, we removed samples with missing values in the target variable, resulting in a dataset of 14,058 samples. Further refinement involved dropping samples with \"Don't know\" or \"No response\" values, resulting in 14,044 samples. Furthermore, because addressing missing data points is critical for reliable model predictions, we employed different strategies to fill in missing values based on the feature data type [25]. For categorical features that represent non-numeric data, we filled missing values with the mode value. We imputed missing values for numerical features using the mean value. Afterward, duplicate entries were identified and removed from the dataset, resulting in a reduction to 13,821 unique samples. Next, we converted features with mixed or inconsistent data types to appropriate numerical formats (float, integer, or string) according to their intended meaning. Outliers, which can skew model results significantly, were identified and removed using the interquartile range (IQR) method [26]. Finally, we obtained a clean and well-organized dataset comprising 13,274 samples. After repeated statistical analysis and data preprocessing, we chose the 10 most significant features out of 19. Table 1 describes the lists of selected variables or features. Out of 10 variables, 9 are independent variables, and 1 is a dependent variable. The independent variables include different factors related to the child, household, and parenting practices. The information collected includes numerical values like the child's weight (Child's_weight) and weight-for-age percentile (Weight_age_P). It also includes categorical variables that indicate disciplinary actions taken by parents (Took_away_privileges, Shook_child, Beat_hard, Called_dumb_lazy), the child's sex, wealth index quintile (Wealth_index), and the mother's education level (Mother's_education). The disciplinary action variables (Removed_privileges, Shook_child, Beat_hard, Called_dumb_lazy) represent different behaviors relevant to parenting practices. However, the dependent variable \"Learning_skill\" indicates whether or not the child has learning difficulties compared to other children of the same age. We combined these variables to create a comprehensive dataset to explore the child's learning difficulties based on the relationship between parenting practices, sociodemographic factors, and children's learning outcomes. The MICS 2019 dataset is unbalanced because learning difficulties in children under five are relatively lower compared to those without such difficulties. This imbalance is a common characteristic of large-scale datasets dealing with minority outcomes, as such cases naturally occur less frequently.