Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data.

ABSTRACT:

Background

Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.

Objective

The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.

Methods

We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced dat

SUBMITTER: Chen KC 

PROVIDER: S-EPMC10790556 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature