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

Abstract

BackgroundAtheoretical 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.MethodsThe 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.ResultsAfter 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).ConclusionThe 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.

Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample

“…This technique has been previously used to identify biomarkers associated with depression [36] and to describe lifestyle clusters associated with depression [18] using data from the NHANES study. Depression was considered as a binary outcome and run for each key cluster using Friedman’s Multiple Additive Regression Trees (MART) boosted algorithm [37,38].…”

“…Depression was considered as a binary outcome and run for each key cluster using Friedman’s Multiple Additive Regression Trees (MART) boosted algorithm [37,38]. Consistent with previous research using this ML algorithm on the 2009 to 2010 NHANES data [36], validation was performed using a random split of each data set into 60% training and 40% validation, a regularization shrinkage parameter of 0.001, with 50% of the residuals used to fit each successive tree (50% bagging) [37]. The maximum number of boosting interactions (i.e.…”

“…five) and within the recommended range [35]. Whilst this technique has been used for predictive purposes [16], it also has the ability to be used as a variable selection method [36]. This method was used as a variable selection technique to identify the prominent medical symptoms associated with depression within the key clusters [37].…”

Biomarkers for Depression: Recent Insights, Current Challenges and Future Prospects

“…The authors conclude that big data can be used effectively for generating hypotheses. 177 Larger biomarker phenotyping projects are now underway and will help to advance our journey into the future of the neurobiology of depression.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…Deep Learning (word2vec) [299] Research Articles [299] Depression DT [303], kNN [134,298], NN [295], Regression [294,296], RF [134], SVM [134], Linear Discriminant Analysis [134] Survey [296,303,304], Social Media [298], Electronic Health Records [295], Imaging [134,294], Biological [134,296] Healthy Ageing RF [304] Survey [304] Psychosis SVM, Multiple Kernel Learning [297] Imaging [297] Schizophrenia RF [291], SVM [291,293], Linear Discriminant Analysis [291], kNN [291] Insurance [291], Imaging [293] Substance Use Topic modelling [306] Interview [306] Symptom Severity NN [301] Clinical Notes [301] Wellbeing BN [302], SVM [302], Deep Learning (paragraph2vec) [300], NN [307] Clinical Notes [300,302] As an emerging field, there are understandably significant gaps for future research to address. It is evident that the majority of papers focus on diagnosis and detection, particularly on depression, suicide risk and cognitive decline.…”