Prediction of Ascending Aortic Dilation and Analysis of Influencing Factors in Bicuspid Aortic Valve Patients Using an Explainable Machine Learning Model
Sijing He, Ning Yan, Guixuan Nie, Fang Wang, Xingyue Yang, Lili Wang, Lisha Na · 2026
Read Prediction of Ascending Aortic Dilation and Analysis of Influencing Factors in Bicuspid Aortic Valve Patients Using an Explainable Machine Learning Model by Sijing He, Ning Yan, Guixuan Nie, Fang Wang, Xingyue Yang, Lili Wang, Lisha Na (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13263703.txt.
Description
Sijing He and co-authors report a 2026 journal article building a machine learning model to predict widening of the aorta in people born with a two-leaflet aortic valve. This valve defect, bicuspid aortic valve, often leads to silent, dangerous enlargement of the body's main artery. The team studied 102 patients with ultrasound, clinical data, and blood enzyme measures.
Statistical screening singled out three predictors: age, a blood protein called MMP-2, and peak blood speed through the valve. A gradient boosting model performed best, scoring very high accuracy on held-out patients. An explanation method showed MMP-2 drove predictions most, followed by age. The authors offer the tool for earlier, targeted monitoring.
Catalog record
| CREATOR | Sijing He, Ning Yan, Guixuan Nie, Fang Wang, Xingyue Yang, Lili Wang, Lisha Na |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40695 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |