## 4 Experiments
### Model Comparison by Model Type  
To thoroughly evaluate the performance of different ML models, as per Figure 10, we compared their effectiveness across three target variables using different feature subsets, as depicted in Figure 11. Key metrics, including AUC, accuracy, sensitivity, and specificity, were analyzed to determine the most effective feature combinations for each target. For the Diagnosis target, all models exhibited superior performance with the Laboratory and Imaging and Literature-Based feature sets. LR and XGBoost reported similar AUC values for Laboratory and Imaging features (0.9862) and Literature features (0.9652), with RF also favoring these sets but with slightly different values (0.9868 and 0.9712, respectively). MLPs showed the highest AUCs for Laboratory and Imaging (0.9862) and Literature-Based features (0.9652). In contrast, models based on Demographic and Morphological features consistently underperformed, with the lowest AUC (0.5472) and accuracy (59.62%). Figure 9: Classification accuracy by model type and feature sets. (**a**) Model type—Diagnosis; (**b**) Feature set—Diagnosis; (**c**) Model type—Management; (**d**) Feature set—Management; (**e**) Model type—Severity; (**f**) Feature Set—Severity. For Management, Clinical Symptoms and Observations and Literature-Based feature sets were notably effective across all models. LR and XGBoost achieved high AUCs of 0.9457 and 0.9585, respectively, for these feature sets. Similarly, RF ranked these features highly, though with slightly lower AUCs (0.9353 and 0.9596). The Demographic and Morphological feature set consistently performed poorly, reflecting its limited utility in predicting management strategies. When predicting Severity, Literature-Based and Clinical Symptoms and Observations feature sets achieved the highest performance across all models. MLPs and XGBoost reported exceptional results for Literature-Based features, with AUCs of 0.9986 and 0.9965, respectively. LR and RF showed strong performance but with slightly lower AUC values compared with XGBoost and MLPs. Consistent with other targets, Demographic and  
Figure 11: ROC Curves for different targets and feature sets. (**a**) LR; (**b**) RF; (**c**) XGBoost; (**d**) MLPs. Note: Due to space limitations, “Morphological” features in the legend also include “Demographic” features. Figure 10: Model comparison by model type. Morphological features produced weaker results, particularly in distinguishing between severity levels. One of the critical aspects of developing machine learning models for clinical applications is the careful selection of appropriate algorithms. In this study, we prioritized models that are well-suited for structured clinical data, including _logistic regression_ (LR), _random forest_ (RF), _gradient boosting machines_ (GBMs), and _Multilayer Perceptrons_ (MLPs). These models were chosen based on their ability to handle structured tabular data, their interpretability, and their established performance in medical decision support tasks. The use of deep learning architectures such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) was considered; however, such models typically require significantly larger datasets with high-dimensional features, such as imaging or sequential data, to generalize effectively. Given that our dataset consists primarily of structured clinical variables rather than unstructured data, deep learning models were not the primary focus of this study. Moreover, deep architectures often demand extensive hyperparameter tuning and increased computational resources, which may not be optimal for real-time clinical decision support in resource-limited settings. Support Vector Machines (SVMs) were also considered due to their strong theoretical foundation in high-dimensional classification problems. However, in preliminary experiments, SVMs exhibited computational inefficiencies, particularly with larger sample sizes and feature-rich datasets.