Show simple item record

dc.contributor.authorDipnall, JF
dc.contributor.authorPasco, JA
dc.contributor.authorBerk, M
dc.contributor.authorWilliams, LJ
dc.contributor.authorDodd, S
dc.contributor.authorJacka, FN
dc.contributor.authorMeyer, D
dc.date.accessioned2020-12-22T05:22:15Z
dc.date.available2020-12-22T05:22:15Z
dc.date.issued2016-02-05
dc.identifierpii: PONE-D-15-43099
dc.identifier.citationDipnall, J. F., Pasco, J. A., Berk, M., Williams, L. J., Dodd, S., Jacka, F. N. & Meyer, D. (2016). Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression. PLOS ONE, 11 (2), https://doi.org/10.1371/journal.pone.0148195.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/258272
dc.description.abstractBACKGROUND: 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.
dc.languageEnglish
dc.publisherPUBLIC LIBRARY SCIENCE
dc.titleFusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0148195
melbourne.affiliation.departmentPsychiatry
melbourne.affiliation.departmentMedicine and Radiology
melbourne.source.titlePLoS One
melbourne.source.volume11
melbourne.source.issue2
dc.rights.licenseCC BY
melbourne.elementsid1036252
melbourne.contributor.authorBerk, Michael
melbourne.contributor.authorPasco, Julie
melbourne.contributor.authorWILLIAMS, LANA
melbourne.contributor.authorDodd, Seetal
melbourne.contributor.authorJacka, Felice
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record