title: 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 included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.
  
  
  RESULTS
  After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).
  
  
  CONCLUSIONS
  The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.
authors: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Melbourne, VIC, Australia.
authors: Joanna F
authors: JF
authors: Dipnall
authors: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|Department of Medicine, The University of Melbourne, St Albans, VIC, Australia.|Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.|University Hospital Geelong, Barwon Health, Geelong, VIC, Australia.
authors: Julie A
authors: JA
authors: Pasco
authors: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|University Hospital Geelong, Barwon Health, Geelong, VIC, Australia.|Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.|Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia.|Orygen, the National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.
authors: Michael
authors: M
authors: Berk
authors: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
authors: Lana J
authors: LJ
authors: Williams
authors: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|University Hospital Geelong, Barwon Health, Geelong, VIC, Australia.|Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.
authors: Seetal
authors: S
authors: Dodd
authors: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.|Centre for Adolescent Health, Murdoch Children's Research Institute, Melbourne, Australia.|Black Dog Institute, Sydney, Australia.
authors: Felice N
authors: FN
authors: Jacka
authors: Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Melbourne, VIC, Australia.
authors: Denny
authors: D
authors: Meyer
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001663:Bilirubin / Q000097:blood*; D015415:Biomarkers / Q000032:analysis / Q000097:blood; D001786:Blood Glucose / Q000502:physiology*; D057225:Data Mining / Q000379:methods*; D003866:Depressive Disorder / Q000097:blood* / Q000473:pathology / Q000523:psychology; D004909:Erythrocyte Indices / Q000502:physiology*; D005260:Female; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009749:Nutrition Surveys; D055815:Young Adult
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D001663:Bilirubin