Development and validation of an interpretable machine learning model for early hospital-based differentiation of chikungunya and dengue fever using routine clinical data.

backgroundChikungunya fever (CHIKF) and dengue fever are mosquito-borne viral diseases. These infections often circulate in the same regions at the same time. Early symptoms can look very similar between the two diseases. This overlap makes early and accurate diagnosis difficult. Many endemic clinics do not have easy access to molecular tests such as RT-PCR [1, 2]. Clinicians therefore need other practical tools for early decision-making. In this study, we aimed to develop an interpretable machine learning model. The model uses routine clinical signs and standard laboratory results. We also aimed to validate the model for differentiation of CHIKF from dengue fever at initial hospital-based assessment.

methodsThis retrospective observational study analyzed 1,058 laboratory-confirmed arboviral infections, including 366 patients with CHIKF and 692 patients with dengue fever. The dataset was stratified by diagnosis and randomly divided into a training set (n = 742) and a held-out test set (n = 316) at a 7:3 ratio. The team collected clinical symptoms, complete blood count (CBC) results, and inflammatory marker data. The team then used these variables to build eight machine learning models. The study evaluated model performance with discrimination metrics. The study also assessed calibration. The study further used decision curve analysis to estimate clinical usefulness. The team examined feature importance with Shapley Additive Explanations (SHAP). The team deployed the best-performing model as a web-based clinical decision-support tool.

resultsAmong the tested approaches, the gradient boosting model (GBM) showed the best and most consistent performance. The GBM achieved a high area under the ROC curve (AUC) in both the training and test sets. The GBM also delivered strong sensitivity and specificity across both cohorts. The SHAP analysis repeatedly highlighted platelet count (PLT) and rash as the most important predictors of CHIKF. These findings match well with known clinical patterns. The online deployment integrated the final model into a simple platform. The platform provides automated, real-time risk estimates using only a small set of routinely available variables.

conclusionThis study shows that interpretable machine learning models can help clinicians distinguish CHIKF from dengue fever early. The models rely on routine clinical information and standard laboratory tests. These inputs are widely available in many settings. The study also presents a web-based tool that applies the best model at the bedside. The tool may be especially useful in resource-limited clinics. However, the tool still needs external validation. Future studies should test the model in multicenter, prospective cohorts.