## 2 Materials and Methods  
### Data  
In this study, we used the publicly available maternal health risk dataset, which includes 1014 records of maternal health data collected from pregnant women in rural areas of Bangladesh through the IoT-based risk monitoring system [31]. It includes features such as age, systolic and diastolic blood pressure, heart rate, and blood glucose levels, along with a target variable that classifies maternal health risk into three categories: low, medium, and high. Table 1 presents descriptive characteristics of predictor variables.  
The data did not include missing values, and no imputation methods were required. The dataset is multi-class (low risk, medium risk, high risk) but exhibits class imbalance, as low-risk cases dominate younger age groups. In contrast, high-risk cases are more prevalent among older maternal ages and elevated blood sugar or blood pressure levels. In the dataset, each record corresponded to an individual patient record at a single point and did not include time series or longitudinal tracking. The publicly available data is anonymized to ensure privacy, aligning with ethical standards for using protected health information (PHI), and is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license for reuse. Throughout the study, all the features were retained, and we tested the models with and without feature scaling but did not observe any significant improvements. Moreover, Random Forest, XGBoost, and CatBoost are tree-based algorithms that are unaffected by the input features' scale as they work on the order and ranking of the features. Finally, for all the different models evaluated in this study, we used an 80:20 train test split with stratified sampling beyond the random sampling to ensure a proportional representation of all risk levels (low/medium/high) in both subsets, mitigating class imbalance effects during evaluation while preserving the dataset's inherent distribution. Given the dataset's limited scope of six features, we did not eliminate any features to retain all potentially meaningful variables. However, we evaluated the relevance of each feature through two complementary techniques: an F-value comparison to assess linear relationships and a Mutual Information Score comparison to capture potential non-linear dependencies. Figure 1 below shows that Blood Sugar (BS) consistently ranked highest across both methods, indicating a strong relationship with the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Units** & **Average** \\ \hline Age & years & 29.9 \(\pm\) 13.5 \\ Systolic BP & mmHg & 113.2 \(\pm\) 18.4 \\ Diastolic BP & mmHg & 76.5 \(\pm\) 13.9 \\ Body Temp & F & 98.7 \(\pm\) 1.4 \\ Heart Rate & bpm & 74.3 \(\pm\) 8.1 \\ Blood Glucose (2-h) & mmol/L & 8.7 \(\pm\) 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of predictor variables.  
target variable. Systolic BP and Age also showed notable relevance, particularly in the Mutual Information analysis, which suggests non-linear contributions. Future research should expand the feature set and incorporate more rigorous importance assessments to evaluate each variable's contribution and clinical implications in a broader context.  
A prior study using this dataset utilized a logistics model tree (LMT) for maternal health risk prediction [31]. While their predictions were highly accurate, our research focuses on improving this by exploring better approaches, such as ensembling and different sampling methods, while using multiple performance metrics for model evaluation.