Interpretable machine-learning prognosis of mycetoma from routine clinical data

Mycetoma is a chronic tropical infection with limited tools for predicting treatment outcomes.

Routinely collected demographic, clinical, imaging, etiological, treatment, and follow-up data from 1,084 patients at the Mycetoma Research Centre were used to evaluate supervised machine learning models for outcome prediction. Binary and three-class tasks (cured, recurrence, disability) were assessed using stratified five-fold cross-validation with preprocessing, imputation, scaling, and class balancing. Logistic regression, support vector machine, and random forest models were compared.

Treatment mode, treatment duration, disease duration at presentation, adherence, lesion size, and imaging findings were the most important predictors. In eumycetoma, random forest achieved the best binary performance (accuracy 0.760 ± 0.036; ROC-AUC 0.835 ± 0.036). In actinomycetoma, random forest achieved accuracy of 0.719 ± 0.115 and ROC-AUC of 0.779 ± 0.138. For the three-class task, random forest performed best in eumycetoma (accuracy 0.612 ± 0.033; macro-AUC 0.777 ± 0.029), while logistic regression performed best in actinomycetoma (accuracy 0.735 ± 0.097; macro-AUC 0.713 ± 0.092).

Standard clinical and imaging variables can support machine learning-based risk stratification in mycetoma, potentially improving early management and follow-up. External validation and clinical utility assessment are still required.