## 4 Experiments
### Models Parameters  
Table 1 summarizes the key parameters used for each model. For logistic regression, no explicit hyperparameter tuning was required as the model was used with its default settings. In the case of random forest, the number of trees ('ntree') was set to 100 to strike a balance between performance and computational efficiency, while the number of features per split  
Figure 8: Severity—GBM. Figure 7: Management—GBM. ('mtry') followed the standard heuristic of the square root of the total number of features. For XGBoost, the tree depth ('max_depth') was fixed at six to prevent overfitting while maintaining sufficient model complexity, and the learning rate ('eta') was set to 0.1 to ensure stable convergence. The number of boosting rounds ('nrounds') was determined through cross-validation, selecting a value of 100 based on optimal performance. For the Multilayer Perceptron, the number of hidden units ('size') was set to five after testing different values to prevent overfitting while preserving expressiveness, and the regularization weight ('decay') was introduced to improve generalization. The maximum number of iterations ('maxit') was set to 200 to allow sufficient training without excessive computational cost. These choices were informed by prior research, standard heuristics, and empirical evaluation, ensuring a trade-off between accuracy, interpretability, and efficiency. The choice of models in the experiments reflects a well-considered approach to addressing the complexities of diagnosing pediatric appendicitis, given the need for both predictive accuracy and interpretability. LR was chosen as a foundational model due to its simplicity and effectiveness in binary classification problems, particularly in healthcare contexts where model interpretability is crucial. LR allows for clear insights into the influence of individual predictors, which is useful for clinicians needing to understand the relationship between clinical features and appendicitis diagnosis. RFs were included because they offer a robust ensemble approach, capable of capturing complex relationships between features without overfitting, which is critical when dealing with potentially noisy medical data. The RF algorithm is particularly well-suited for datasets with many features, as it can handle high-dimensional spaces efficiently and produce reliable predictions. Moreover, the use of feature importance scores from the RF model adds another layer of interpretability, allowing the identification of the most relevant predictors for diagnosing appendicitis. The inclusion of XGBoost demonstrates a commitment to maximizing predictive performance. XGBoost has become a popular model in ML due to its ability to handle a wide range of data types and its effectiveness in boosting weak learners to create strong, accurate models. In the context of this study, XGBoost was selected for its ability to optimize performance through hyperparameters such as learning rate and tree depth, making it particularly useful for refining predictions in a complex clinical dataset where nuanced decision boundaries are needed to differentiate between cases. MLPs are known for their ability to model highly non-linear relationships, which may exist in complex medical datasets. While MLPs are often less interpretable than other models like LR or RFs, their capacity for handling intricate patterns in data makes them valuable in cases where traditional models may fall short. In this study, the MLP was employed to assess whether techniques based on neural networks could further enhance \begin{table}
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Table 1: Summary of model parameters used in the experiments. diagnostic precision, particularly in instances where other models might struggle to capture complex interactions between clinical features. Other models could have been considered.