Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data.

ABSTRACT: As awareness of the habits and risks associated with lung cancer has increased, so has the interest in promoting and improving upon lung cancer screening procedures. Recent research demonstrates the benefits of lung cancer screening; the National Lung Screening Trial (NLST) found as its primary result that preventative screening significantly decreases the death rate for patients battling lung cancer. However, it was also noted that the false positive rate was very high (>94%).In this work, we investigated the ability of various machine learning classifiers to accurately predict lung cancer nodule status while also considering the associated false positive rate. We utilized 416 quantitative imaging biomarkers taken from CT scans of lung nodules from 200 patients, where the nodules had been

SUBMITTER: Delzell DAP 

PROVIDER: S-EPMC6917601 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature