## 1 Introduction  
Children under 5 years old often present with non-specific symptoms, making the diagnosis of acute appendicitis challenging. Communication difficulties and limitations in conducting thorough physical examinations further contribute to the high misdiagnosis rate in this age group. Consequently, delayed diagnosis increases the risk of complications such as perforation and abscess formation. Factors like a thin-walled appendix and inadequate mental barrier also contribute to the likelihood of perforation. The list of potential differentials includes conditions like acute gastroenteritis and respiratory or urinary tract infections. [PERSON] et al. [1] conducted a clinical study highlighting the substantial misdiagnosis rate, ranging from 70 to 100% in children aged 3 and younger, with preschool-aged children experiencing rates of 19 to 57% (with perforation observed in 43% to 72% of cases). This rate decreased to 12 to 28% in school-age children and less than 15% in adolescents. Alarmingly, up to 15% of patients underwent multiple emergency department visits before a correct diagnosis of acute appendicitis was established. Common characteristics among misdiagnosed patients included a relatively short duration of symptoms at the initial visit, presentation during late-night hours, fewer physical examination findings, and inadequate investigation. The risk of misdiagnosis escalates with younger age, with young children facing a five-fold higher risk of complicated appendicitisis.  
In this study, we aim to explore the role of ML algorithms in diagnosing pediatric appendicitisitis, focusing on their ability to predict the severity, management strategy, and likelihood of negative appendectomies. We employ a range of supervised learning techniques to analyze a dataset collected from pediatric patients presenting with suspected appendicitisis. By comparing the performance of models such as logistic regression (LR), random forests (RFs), gradient boosting machines (GBMs), and Multilayer Perceptrons (MLPs), we seek to demonstrate the potential of ML to enhance diagnostic accuracy and provide actionable insights in clinical practice.  
This research not only contributes to the growing body of literature on the application of ML in healthcare but also aims to inform future efforts to integrate these models into clinical workflows. Our findings underscore the need for further validation of ML-based tools in larger, more diverse patient populations, as well as the importance of addressing practical and ethical concerns in their implementation.  
In particular, the originality and contribution of this work can be denoted as follows:  
* The study applies ML models to pediatric appendicitisis diagnosis, which is an area with limited existing research focused on this specific patient group;
* We introduce a novel feature selection approach that combines clinical, laboratory, and imaging variables to improve diagnostic accuracy and predict appendicitisis severity;
* The research evaluates multiple ML algorithms, offering a comparative analysis of their performance in clinical settings;
* Our work addresses the challenge of negative appendectomies by incorporating models designed to predict unnecessary surgeries, providing insights that could reduce surgical interventions and improve patient outcomes;
* This study lays the groundwork for the future integration of ML models into clinical workflows, emphasizing practical considerations such as interpretability and clinical relevance.  
The remainder of this paper is structured as follows. Section 2 reviews the related work, while Section 3 discusses the theoretical background related to pediatric appendicitisis and the deployed ML models. Section 4 presents a detailed overview of the dataset and the methodology used for feature selection and model training. Furthermore, it discusses the experimental setup, including the evaluation metrics and cross-validation techniques. Finally, Section 5 concludes the paper and offers suggestions for future research directions.