Asset Details
Do you wish to reserve the book?
Machine learning-based prediction of primary aldosteronism subtype using comprehensive clinical features
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine learning-based prediction of primary aldosteronism subtype using comprehensive clinical features
Do you wish to request the book?
Machine learning-based prediction of primary aldosteronism subtype using comprehensive clinical features
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Machine learning-based prediction of primary aldosteronism subtype using comprehensive clinical features
2026
Overview
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.
