Key result
Machine learning models achieve 94% accuracy in predicting appendicitis severity, outperforming traditional logistic regression.
Why the study?
Timely diagnosis and accurate severity assessment in pediatric appendicitis are critical to prevent complications, motivating machine learning approaches to improve severity and management prediction.
Do machine learning approaches improve the prediction of disease severity and management in pediatric appendicitis compared to traditional logistic regression?
Observational
Do machine learning approaches improve the prediction of disease severity and management in pediatric appendicitis compared to traditional logistic regression?
Machine learning models using clinical markers can accurately predict disease severity and management needs in pediatric appendicitis, outperforming traditional logistic regression.
ML may aid pediatric appendicitis prediction; hypothesis-generating and requires prospective validation before practice change.
Appendicitis remains a common surgical emergency in children and adolescents, where timely diagnosis and accurate severity assessment are critical to prevent complications such as perforation and peritonitis. In this study, we applied machine learning (ML) approaches to clinical data to enhance prediction of both disease severity and the need for surgical versus conservative management. Compared with traditional logistic regression, ML models demonstrated improved predictive performance, particularly in identifying high-risk patients. The most influential variables included length of hospital stay, loss of appetite, C-reactive protein (CRP), presence of peritonitis, and the Alvarado score. These predictors, when analyzed through ML, allowed better stratification of patients into severe versus non-severe categories, achieving 94 % accuracy for severity prediction and 89 % accuracy for management prediction. Explainable AI techniques further clarified the role of each variable, ensuring interpretability for clinicians. Our findings highlight that ML-based decision support tools can complement conventional clinical assessment, potentially reduce diagnostic delays and supporting more precise treatment planning in hospital settings.
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Manohar et al. (2025) conducted an observational in Pediatric appendicitis. Machine learning models vs. Traditional logistic regression was evaluated on Prediction of disease severity and management. Machine learning models achieved 94% accuracy for predicting appendicitis severity and 89% accuracy for management prediction, outperforming traditional logistic regression.
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