## 4 Experiments
### Model Comparison by Model Type
Additionally, tree-based ensemble models such as RF and GBM provided superior interpretability, an essential factor in clinical decision-making, allowing the identification of key predictive features contributing to diagnostic accuracy. The selection of machine learning models is ultimately guided by the trade-off between predictive performance, interpretability, and computational feasibility. The findings of this study reinforce the suitability of ensemble learning methods for structured clinical data while acknowledging the potential for deep learning in more complex, multimodal datasets. Another key aspect of this study was the rigorous feature selection process, which ensured that only the most relevant predictors were included in the machine learning models. However, as with any data-driven approach, preprocessing decisions, such as handling missing values, can introduce potential biases that warrant careful consideration. Missing data were handled using established imputation techniques, including mean imputation for continuous variables and mode imputation for categorical variables when appropriate. While these methods prevent data loss and allow for the inclusion of a larger sample size, they also assume that missingness is random (_Missing Completely at Random_, MCAR), which may not always hold in clinical datasets. If certain variables have systematic missingness (e.g., laboratory test results not being recorded for milder cases), the imputation strategy could introduce bias, particularly by underrepresenting specific patient subgroups. Another critical preprocessing step was outlier detection and removal, which helps improve model robustness but may also lead to the exclusion of rare but clinically significant cases. For example, extreme values in laboratory markers could correspond to severe cases of appendicitis, and their removal could influence model sensitivity in detecting such cases. To mitigate these potential biases, we performed sensitivity analyses, comparing model performance with and without imputed values to assess the impact of missing data handling. The results indicated that, while imputation slightly affected individual feature importance rankings, overall model performance remained stable, suggesting that the selected imputation strategy did not substantially alter the predictive capabilities of the models. A further challenge consists of the _interpretability_ of complex models such as _random forest_, _XGBoost_, and _Multilayer Perceptron_, which, despite their high predictive power, often function as black-box systems. Clinicians require transparent decision-making processes to trust and effectively utilize ML-based recommendations. To address this, future research should focus on integrating _explainability techniques_ to provide insights into how these models weigh different clinical features. Another significant challenge is the _deployment of ML models into hospital systems_. Electronic Health Records (EHR) systems vary across institutions, requiring seamless integration of ML models to ensure real-time decision support without disrupting clinical workflows. Technical barriers such as data standardization, interoperability between different EHR platforms, and computational infrastructure requirements must be addressed before these models can be effectively implemented in a clinical setting. Additionally, the success of ML applications in healthcare depends not only on technical feasibility but also on _clinical adoption and training_. Many healthcare professionals may be unfamiliar with ML-based decision support tools, necessitating targeted training programs to improve their understanding of model predictions and limitations. Without adequate training, there is a risk of either over-reliance on ML outputs or outright rejection due to skepticism. Future implementations should consider collaborative frameworks between data scientists and clinicians to ensure usability, trust, and proper interpretation of ML-driven insights.