2026-10-18T23:15:20Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/100832026-10-18T20:41:14Zopenairedrivercom_ebyu_10000col_ebyu_10001

A Novel Biomarker-Based Decision Support System for Pediatric Appendicitis Diagnosis: A Comparative Study of Ensemble Models AlgorithmsAvci, VeliHuyut, Mehmet TahirVelichko, AndreiBelyaev, MaksimAppendicitisBiomarkersBoosting and bagging based ensemble modelsClinical decision support system (CDDS)SHAPSMOTEbiological markerC reactive proteinabdominal painadolescentadultarithmeticArticlechildclinical decision support systemcohort analysiscomparative studycorrelation coefficientcross validationdecision treediagnostic accuracydiagnostic test accuracy studyelectronic health recordemergency wardensemble learningfalse negative resultfemalehistopathologyhumaninfantIntroduction: Acute appendicitis is the commonest cause of surgical abdominal pain, yet diagnosis in children remains challenging; delays increase the risk of perforation, peritonitis and sepsis. We sought to develop a rapid, inexpensive and interpretable clinical-decision support system (CDSS) that leverages routine blood tests (RBT) to assist early paediatric triage. Materials and Methods: In this retrospective single-centre study (January 2020–December 2024) we analysed 275 emergency-department encounters for abdominal pain (75 histology-confirmed appendicitis, 200 controls). The six-stage pipeline comprised (1) cohort selection; (2) exploratory logistic-regression screening of RBT variables; (3) training of Random Forest, Gradient Boosting and LightGBM ensembles (with/without SMOTE) under 10 × 10 stratified cross-validation; (4) SHAP-based feature interpretation; (5) exhaustive generation of every two- and three-parameter arithmetic biomarker from seven RBT features; and (6) derivation of probability-threshold curves and a three-zone rule tree for the top biomarker. Performance was reported with accuracy (ACC), Matthews correlation coefficient (MCC), AUC-ROC, sensitivity, specificity, F1-Score PPV and NPV. Results: Logistic regression and SHAP confirmed CRP, WBC and neutrophil count as strong positive predictors, whereas MPV and PDW were protective; PLT remained non-informative. All three ensemble classifiers surpassed 97% accuracy, 98% AUC-ROC and 0.93 MCC, with no gain from SMOTE. An extensive formula search, the best two-parameter marker was Neutrophil ÷ PDW (MCC = 0.73, specificity 95%). Its ensemble curve crosses P = 0.5 five times; practical cut-offs of < 0.633 (strongly indicate healthy) and > 0.794 (strongly indicate appendicitis) retain high NPV (∼91%) and PPV (∼86%). Among triple formulas that do not rely on PLT, the leading biomarker was CRP+WBC+Neutrophil (MCC = 0.85, PPV 92%, NPV 95%). The ensemble curve intersects at P = 0.5 at three points; values >27 strongly predict appendicitis, <23 indicates a healthy state, and values 23–27 leave a small uncertain band. A rule-based CDSS built on these two biomarkers correctly classified all controls (specificity 100%), sensitivity 95%, achieved 91% overall accuracy, and offers interpretable, electronic health records (EHRs)-ready cut-offs for paediatric appendicitis triage. Conclusion: Routine haematology-biochemistry data, interpreted through ensemble learning and engineered biomarkers, can deliver fast, transparent and highly accurate support for paediatric appendicitis triage. Given its zero false-positive rate, the proposed CDSS is best suited to in-hospital monitoring, where minimising false negatives is critical. Prospective multi-centre validation is warranted. © 2025 AGBMElsevier Masson s.r.l.2026Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/10083https://doi.org/10.1016/j.irbm.2025.1009211959-0318enIRBMinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/10083