## 5 Conclusions and Future Work  
This work addressed the diagnostic challenges of pediatric appendicitis through the application of machine learning (ML) techniques to enhance both diagnostic accuracy and patient management. Pediatric appendicitis is complex to diagnose due to age-related symptom variations, communication barriers, and the absence of standardized diagnostic guidelines, especially for children under five. These factors, coupled with non-specific symptoms in younger children and females, often result in misdiagnoses, delayed treatments, and unnecessary surgical interventions. Our findings demonstrated that ML algorithms--including _logistic regression_ (LR), _random forest_ (RF), _XGBoost_, and _Multilayer Perceptron_ (MLP)--achieved high AUC and accuracy values, confirming their robustness and reliability. Consistent with clinical expectations, key symptoms such as neutrophil, nausea, loss of appetite, coughing pain, and migratory pain, along with established literature-based features like the Alvarado Score, Pediatric Appendicitis Score, appendix diameter, C-reactive protein, and White Blood Cell count emerged as the most effective predictors across all targets. In contrast, demographic and morphological factors such as age, height, weight, and BMI were less predictive, reinforcing the clinical perspective that symptomatology and specific biomarkers provide more direct diagnostic insights compared with general physical characteristics. The main contribution of this work is demonstrating how ML can complement clinical decision-making by providing precise, data-driven insights into pediatric appendicitis. This approach has the potential to reduce negative appendectomies, improve diagnostic precision, and inform treatment strategies, ultimately leading to better patient outcomes. However, several limitations need to be addressed in future research. One key challenge is the incomplete nature of the dataset, particularly regarding ultrasound imaging and laboratory results, which may affect model performance and predictive accuracy. Toimprove model robustness, future research should focus on collecting more comprehensive, high-quality clinical and imaging data. A critical next step is the external validation of the proposed models. While the models performed well on the current dataset, generalizability must be confirmed by validating them on independent datasets from diverse clinical settings. This would ensure that the models maintain their predictive accuracy across varying patient populations and institutional protocols. Moreover, multicenter validation studies would help mitigate biases introduced by a single data source. Future research should also explore domain adaptation techniques to fine-tune the models for different clinical environments while maintaining diagnostic accuracy. Additionally, interpretability remains a challenge for complex ML models such as RF and XGBoost. Although these models achieved high predictive accuracy, their complexity can hinder adoption in clinical practice. Future efforts should aim to improve model transparency using explainability tools such as _SHAPs (Shapley Additive Explanations)_ and _LIME (Local Interpretable Model-Agnostic Explanations)_ to facilitate their integration into clinical workflows. Lastly, exploring novel predictors, such as emerging biomarkers, genetic information, and detailed patient histories, may further enhance diagnostic accuracy, particularly in atypical cases. Future research should also investigate the ethical implications of ML-driven diagnostics, ensuring that AI applications in pediatric healthcare remain transparent, equitable, and aligned with clinical standards. By addressing these challenges, future studies can advance the reliability and applicability of ML models in pediatric appendicitis diagnosis, paving the way for AI-assisted decision support tools that optimize patient care. Conceptualization, D.M. and E.B. ; methodology, D.M. ; software, D.M. ; validation, E.B., D.M. and A.G.; formal analysis, E.B. ; investigation, D.M.