Machine learning-based prediction of primary aldosteronism subtype using comprehensive clinical features.

Primary aldosteronism (PA) has two major subtypes: unilateral (uPA) and bilateral (bPA). Although several diagnostic models for subtype classification have been reported, the optimal combination of algorithms and clinical features remains unclear. This study aimed to identify machine learning models and clinical features that contribute to PA subtype prediction. A total of 274 PA patients who underwent successful adrenal venous sampling (AVS) at a single center were analyzed. Overall, 196 endocrine features were comprehensively collected and classified into four categories: A, PA-related features; B, challenge tests; C, general biochemistry; and D, urinary steroid profile. Five machine learning algorithms were applied; predictive performance of the models as well as predictive contribution of features and categories were evaluated. Among the models, the random forest (RF) model achieved the highest predictive accuracy (91.3%). The most contributing feature in the RF model was plasma aldosterone concentration after the captopril challenge test (CCT90-PAC). Category B made the greatest contribution to RF, followed by Categories A, D, and C. Combining Categories A and B improved predictive performance. These findings indicate that machine learning models, particularly RF, are effective for PA subtype prediction, with challenge test-related features in Category B making a major contribution.