Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction

IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, characterized by immune complex deposition in the glomerular mesangium, leading to mesangial hypercellularity, persistent microhematuria, proteinuria, and progressive renal impairment. Given its common occurrence, diagnosis normally involves renal biopsy, with its accompanying risks of bleeding and infection. In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN.

This retrospective study included 292 patients with IgAN and 310 individuals with different nephropathies, utilizing 82 clinical variables, with kidney pathology results serving as ML labels. A random forest (RF) regression model addressed missing values. Subjects were divided into a development set (n = 542) and a test set (n = 60). The RF method was applied to select 17 key features for building diagnostic models, including the RF model, support vector machine (SVM), adaptive boosting (ADB), and traditional doctor judgment. Performance was evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC) from receiver operating characteristic (ROC) analyses.

The random forest model performed best with an accuracy of 82.3% and an AUC of 0.89 on the test set, outstripping SVM with an AUC of 0.82 and ADB with an AUC of 0.88. High urinary protein, low serum albumin, and elevated IgG levels were the primary features correlated with IgAN.

In this study, a non-invasive diagnostic model for IgAN was developed, with RF line and superior accuracy and clinical applicability. This further highlights the potential of ML-based approaches in reducing reliance on invasive procedures and providing opportunities for early IgAN diagnosis.