Fusing Data Mining Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

摘要

BackgroundAtheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.