## 4 Experiments
### Model Train and Evaluation  
This section details the process of preparing the dataset for model training and evaluation, focusing on feature selection, data splitting, and model optimization. Data preparation involved encoding categorical variables as binary targets, ensuring consistent factor levels between training and testing sets, and defining feature subsets based on their relevance to the prediction task. Key features identified through Recursive Feature Elimination (RFE) were organized into specific subsets, including Demographic and Morphological variables, Clinical Scoring and Historical features, Laboratory Tests and Imaging variables, Clinical Symptoms and Physical Observations features, and a Literature-Based group that incorporated insights from recent studies. After the feature selection process, the Diagnosis model included 19 features, the Management model used 31 features, and the Severity model was trained with 11 features. The models deployed were LR, RF, XGB, and MLP, and each model was trained with these tailored feature subsets for the specific target variables. The models' performance was assessed using confusion matrices, ROC curves, and AUC scores, providing a detailed analysis of their effectiveness. LR served as a baseline model due to its simplicity and interpretability, while RF and XGB offered greater power for capturing non-linear relationships and complex feature interactions. MLPs were tested for their ability to further improve performance through advanced pattern recognition. An SVM model was also evaluated but ultimately removed from the experiments due to issues with handling factor levels for the Management target. Our findings demonstrated high AUC, accuracy, sensitivity, and specificity across these models, indicating that they effectively captured patterns in the data and met the project's analytical goals. Key performance metrics from the experiments underscored the robustness of these algorithms, which successfully addressed the complexities of our clinical dataset and managed the mixed data types effectively. Given the success of these models, we concluded that additional, newer algorithms were unlikely to add substantial performance improvements without complicating interpretability. We also observed that feature selection significantly impacted model performance across the different targets. For Diagnosis and Management, a larger feature set (19 and 31, respectively) contributed to greater model accuracy (96.93% and 91.95%) and Kappa values (0.9363 and 0.8340), aligning with the complexity of these tasks. Conversely, Severity was accurately predicted with a more focused subset of 11 variables, reflecting the specialized nature of this classification. This targeted approach to feature selection effectively optimized model performance, balancing accuracy and interpretability across all tasks. To further validate the feature selection process, we compared the MDG and RI scores of excluded versus retained features, particularly metabolic and environmental factors. Results consistently showed lower importance scores for excluded features, reinforcing the robustness of our RFE-driven selection process. For the Diagnosis target,high-impact variables like appendix diameter (MDG = 147.71, RI = 69.73) greatly outperformed metabolic factors like WBC count (MDG = 20.66, RI = 0.46) and neutrophil percentage (MDG = 15.26, RI = 0.48), as well as environmental factors like age (MDG = 9.93, RI = 0.37) and BMI (MDG = 9.27, RI = 0.14). In the Management target, length of stay (MDG = 104.79, RI = 56.44) and appendix diameter (MDG = 51.22, RI = 10.75) similarly out-weighed metabolic predictors like hemoglobin (MDG = 8.14, RI = 1.32) and environmental ones like body temperature (MDG = 11.02, RI = 0.25). Lastly, for Severity, dominant features such as length of stay (MDG = 74.34, RI = 57.83) and CRP (MDG = 34.10, RI = 14.45) far surpassed BMI (MDG = 10.40, RI = 3.01) and weight (MDG = 9.05, RI = 1.89).