Machine learning-based prediction of shigellosis in children under five: development and internal validation of a prediction model.

OBJECTIVES: Shigella remains a major cause of diarrhoea and mortality in children under five in low- and middle-income countries, where laboratory confirmation is often inaccessible and dysentery-based management lacks sensitivity. This study aimed to develop and internally evaluate machine learning models to predict microbiologically confirmed Shigella infection and secondarily to demonstrate the feasibility of translating the best-performing model into a prototype web-based decision-support application. METHODS: We analysed data from 3356 children with diarrhoea enrolled in the Global Enteric Multicentre Study, excluding co-infections and non-diarrhoeal controls. Multiple machine learning algorithms were trained using 32 predictors and evaluated with 10-fold cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUC), recall and Brier score, with recall prioritised to minimise false negatives. The selected model was simplified using the 10 most informative predictors and integrated into a prototype web-based application. All analyses were restricted to internal validation. RESULTS: Support vector machine (SVM) demonstrated the highest recall (0.64) with modest but potentially useful discrimination (AUC 0.74). The parsimonious SVM model achieved recall 0.67, AUC 0.77 and Brier score 0.16. A web-based prototype was developed to illustrate real-time model outputs for research purposes. DISCUSSION: The model achieved moderate internal performance while prioritising recall, supporting its potential role as a research-stage risk stratification tool in settings with limited diagnostic capacity. CONCLUSION: This internally validated model shows potential for supporting risk stratification for shigellosis. External validation and impact evaluation are required before clinical or operational use.