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

• Published in: PloS one Vol. 11; no. 2; p. e0148195
• Main Authors: Dipnall, Joanna F., Pasco, Julie A., Berk, Michael, Williams, Lana J., Dodd, Seetal, Jacka, Felice N., Meyer, Denny
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.02.2016; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Alcoholic beverages; Algorithms; Alzheimer's disease; Amalgamation; Analysis; Antioxidants; Artificial intelligence; Bilirubin; Bilirubin - blood; Bioindicators; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - analysis; Biomarkers - blood; Blood Glucose - physiology; Body mass; Body mass index; Cancer; Computer and Information Sciences; Control methods; Data mining; Data Mining - methods; Data processing; Depression (Mood disorder); Depressive Disorder - blood; Depressive Disorder - pathology; Depressive Disorder - psychology; Diabetes; Diagnosis; Epidemiology; Erythrocyte Indices - physiology; Female; Food security; Genetic aspects; Glucose; Hospitals; Humans; Identification methods; Inflammation; Learning algorithms; Logistic Models; Machine Learning; Male; Mathematical models; Medical research; Medicine and Health Sciences; Mental depression; Mental health; Methodology; Middle Aged; Missing data; Moderators; Mortality; Multivariate Analysis; Nutrition; Nutrition Surveys; Open access; Physical activity; Physical Sciences; Poverty; Preventive medicine; Psychiatry; Regression analysis; Regression models; Research and Analysis Methods; Sampling methods; Smoking; Statistical analysis; Statistical models; Statistics; Studies; Surveys; Young Adult; Australia; Melbourne Victoria Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0148195

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. 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. 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). 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.