Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data.

ABSTRACT:

Background

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

Methods

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the

SUBMITTER: Liu JJ 

PROVIDER: S-EPMC10811045 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature