## 4 Experiments  
### Dataset Information  
In this section, we present a detailed overview of the dataset used to develop ML models for diagnosing pediatric appendicitis. The dataset is derived from a retrospective study conducted at the Children's Hospital St. Hedwig, with ethical approval from the University of Regensburg's Ethics Committee [17]. It encompasses 782 observations across 58 variables, covering patient demographics (age, sex, height, weight, BMI), clinical scoring systems (Alvarado Score and Pediatric Appendicitis Score), physical examination findings, laboratory test results, and detailed ultrasound observations. Key target variables of the dataset include:  
1. The patient's diagnosis, histologically confirmed for operated patients. Conservatively managed patients were classified as having appendicitis if their AS or PAS was \(\geq\)4, and the appendix diameter was \(\geq\)6 mm. 2. The management approach, determined by a senior pediatric surgeon, was categorized as either operative (appendectomy: laparoscopic, open, or conversion) or conservative (without antibiotics). Patients undergoing secondary surgery after initial conservative management were considered operatively managed. 3. The severity of appendicitis, classified as uncomplicated (subacute, fibrosis) and complicated (phlegmonous, gangrenous, perforated, abscessed). Upon initial exploration, we identified that 30.9% of the data were missing. Notably, variables related to ultrasound findings had at least 40% missing values, a common issue due to operator dependency and variability in emergency settings. Similarly, several urine laboratory tests exhibited high missing rates, likely due to their secondary importance in urgent care scenarios. To address these challenges, variables with over 40% missing data were excluded. As a result, our analysis was focused on a refined dataset comprising 782 observations and 39 variables. After this filtering process, all 782 observations were retained, while only variables with more than 40% missing values were excluded. As a result, the dataset used for modeling included 39 variables instead of the original 58. The remaining gaps (5.5%) were handled using multiple imputation with predictive mean matching (PMM) (m = 5, maxit = 50). While PMM improved data completeness, significant missingness persisted in \"RBC in Urine\", \"Ketones in Urine\", and \"WBC in Urine\" (206, 200, and 199 instances, respectively), indicating limitations in capturing predictive relationships. To enhance imputation reliability, we increased imputations to m = 10, although substantial missing counts remained, prompting us to test other methods. We evaluated linear regression (PMM) and logistic regression (logreg) imputation methods; however, adjustments to the m parameter and model specifications did not fully resolve these gaps. Transitioning to k-nearest neighbors (k-NNs) imputation with Gower distance offered greater flexibility and effectively addressed all remaining missing values in mixed data types. After preprocessing, statistical examination of the cleaned dataset revealed significant variability in patient characteristics. Key findings include:* Morphological characteristics: the cohort had a mean age of 11.35 years, an average height of 148.07 cm, and an average weight of 43.18 kg, with notable diversity in body mass. The average BMI was 18.89 kg/m\({}^{2}\), ranging from underweight to obesity. * Clinical scores: the average AS was 5.96 and the PAS was 5.27, suggesting moderate appendicitis risk. Scores in this range typically indicate that further diagnostic studies may be necessary, as scores between 5 and 8 are inconclusive, while scores of 1 to 4 are usually negative, and scores of 9 to 10 are diagnostic of appendicitis. * Physical examination and laboratory tests: average body temperature was 37.41 \({}^{\circ}\)C, with some unusually low readings affecting the distribution. The mean WBC count was 12.70 \(\times\) 10\({}^{9}\)/L and the mean neutrophil percentage was 71.82%, reflecting typical inflammatory responses.