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

PLOS ONE, Dec 2019

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). Conclusion 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.

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

February Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression Joanna F. Dipnall 0 1 Julie A. Pasco 0 1 Michael Berk 0 1 Lana J. Williams 0 1 Seetal Dodd 0 1 Felice N. Jacka 0 1 Denny Meyer 0 1 0 1 IMPACT Strategic Research Centre, School of Medicine, Deakin University , Geelong, VIC , Australia , 2 Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology , Melbourne, VIC , Australia , 3 Department of Medicine, The University of Melbourne , St Albans, VIC , Australia , 4 Department of Epidemiology and Preventive Medicine, Monash University , Melbourne, VIC , Australia , 5 University Hospital Geelong , Barwon Health, Geelong, VIC , Australia , 6 Department of Psychiatry, The University of Melbourne , Parkville, VIC , Australia , 7 Florey Institute of Neuroscience and Mental Health , Parkville, VIC , Australia , 8 Orygen , the National Centre of Excellence in Youth Mental Health , Parkville, VIC , Australia , 9 Centre for Adolescent Health, Murdoch Children's Research Institute , Melbourne , Australia , 10 Black Dog Institute, Sydney , Australia 1 Editor: Mansour Ebrahimi, Qom University, ISLAMIC REPUBLIC OF IRAN - OPEN ACCESS Data Availability Statement: Original and cleaned data for the NHANES data used in this study is open access and located at the URL http://wwwn.cdc.gov/ Nchs/Nhanes/Search/nhanes09_10.aspx A Stata syntax file containing a template for enabling those to implement this methodology on other data files has been provided as part of the Supplementary information. This template also explains the NHANES biomarker variables used for this study and references key NHANES analytical information. Funding: Michael Berk is supported by a NHMRC Senior Principal Research Fellowship 1059660 and Lana J Williams is supported by a NHMRC Career Methods 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 epidemio 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 Development Fellowship 1064272. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: JFD has no conflicts of interest. JAP has recently received grant/research support from the National Health and Medical Research Council (NHMRC), BUPA Foundation, Amgen, GlaxoSmithKline, Osteoporosis Australia, Barwon Health and Deakin University. MB has received Grant/Research Support from the NIH, Cooperative Research Centre, Simons Autism Foundation, Cancer Council of Victoria, Stanley Medical Research Foundation, MBF, NHMRC, Beyond Blue, Rotary Health, Geelong Medical Research Foundation, Bristol Myers Squibb, Eli Lilly, Glaxo SmithKline, Meat and Livestock Board, Organon, Novartis, Mayne Pharma, Servier and Woolworths, has been a speaker for Astra Zeneca, Bristol Myers Squibb, Eli Lilly, Glaxo SmithKline, Janssen Cilag, Lundbeck, Merck, Pfizer, Sanofi Synthelabo, Servier, Solvay and Wyeth, and served as a consultant to Astra Zeneca, Bioadvantex, Bristol Myers Squibb, Eli Lilly, Glaxo SmithKline, Janssen Cilag, Lundbeck Merck and Servier. Drs Copolov, MB and Bush are co-inventors of provisional patent 02799377.3-2107-AU02 “Modulation of physiological process and agents useful for same”. MB and Laupu are co-authors of provisional patent 2014900627 “Modulation of diseases of the central nervous system and related disorders”. MB is supported by a NHMRC Senior Principal Research Fellowship 1059660. LJW is supported by a NHMRC Career Development Fellowship 1064272. SD has received grants/research support from the Stanley Medical Research Institute, NHMRC, Beyond Blue, ARHRF, Simons Foundation, Geelong Medical Research Foundation, Fondation FondaMental, Eli Lilly, Glaxo SmithKline, Organon, Mayne Pharma and S (...truncated)


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Joanna F. Dipnall, Julie A. Pasco, Michael Berk, Lana J. Williams, Seetal Dodd, Felice N. Jacka, Denny Meyer. Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression, PLOS ONE, 2016, Volume 11, Issue 2, DOI: 10.1371/journal.pone.0148195