Predicting renal damage in children with IgA vasculitis by machine learning.

Background: Children with IgA Vasculitis (IgAV) may develop renal complications, which can impact their long-term prognosis. This study aimed to build a machine learning model to predict renal damage in children with IgAV and analyze risk factors for IgA Vasculitis with Nephritis (IgAVN).

Methods: 50 clinical indicators were collected from 217 inpatients at our hospital. Six machine learning algorithms-Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Support Vector Machine, Decision Trees, and Random Forest-were utilized to select the model with the highest predictive performance. A simplified model was developed through feature importance ranking and validated by an additional cohort with 46 patients.

Results: The random forest model had the highest accuracy, precision, recall, F1 score, and area under the curve, with values of 0.91, 0.98, 0.70, 0.79 and 0.94, respectively. The top 11 features according to the importance ranking were anti-streptolysin O, corticosteroids therapy, antihistamine therapy, absolute eosinophil count, immunoglobulin E, anticoagulant therapy, C-reactive protein, prothrombin time, age at onset, D-dimer, recurrence of rash ≥ 3 times. A simplified model using these features demonstrated optimal performance with an accuracy of 84.2%, a sensitivity of 89.4%, and a specificity of 82.5% in external validation. Finally, we provided a web tool based on the simplified model, whose code was published on https://github.com/mulanruo/IgAVN_Prediction .

Conclusion: The model based on the random forest algorithm demonstrates good performance in predicting renal damage in children with IgAV, providing a basis for early clinical diagnosis and decision-making.