PEX-LC-stage-symptoms

This study characterised and compared background factors and symptoms at diagnosis across patients with non-advanced lung cancer, advanced lung cancer, and cancer-free controls. Univariate logistic regression and multivariate machine learning models (regularized logistic regression, random forest, and extreme gradient boosting) were used to identify variables that contribute to the detection of early- and late-stage lung cancer, and to assess the potential predictive value of detailed patient-reported symptom data and background factors. The current release contains all code used to produce the results and visualizations in the submitted manuscript after revision round 2.