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

ABSTRACT:

Background

Atheoretical 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.

Methods

The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study

SUBMITTER: Dipnall JF 

PROVIDER: S-EPMC4744063 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature