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

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...

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Bibliographic Details
Published in:PloS one 2016-02, Vol.11 (2), p.e0148195-e0148195
Main Authors: Dipnall, Joanna F, Pasco, Julie A, Berk, Michael, Williams, Lana J, Dodd, Seetal, Jacka, Felice N, Meyer, Denny
Format: Article
Language:English
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
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Summary: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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0148195