Diagnostic performance of stacking ensemble combined with SHAP interpretation for benign and malignant pulmonary space-occupying lesions
Researchers developed a stacking ensemble machine learning model combined with SHAP interpretation to improve the diagnostic accuracy of pulmonary lesions. By integrating radiological, clinical, and laboratory data, the study aims to provide a more reliable tool for identifying malignant lung conditions.
Why it matters
Improving the accuracy of early lung cancer diagnosis through AI-driven multimodal analysis can significantly enhance patient survival rates.
Scientific Reports ( 2026 ) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in