Data mining: Potential applications in research on nutrition and health.
Aim: Data mining enables further insights from nutrition ‐ related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition ‐ related intervention. Methods: Baseline dat...
| Published in: | Nutrition & Dietetics Vol. 74; no. 1; pp. 3 - 11 |
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| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Feb2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121063185&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121063185 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14466368 QMK jtl: Nutrition & Dietetics issn: 14466368 maglogo: Y pubinfo: dt: Feb2017 vid: 74 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 121063185 121063185 121063185 10.1111/1747-0080.12337 121063185 ppf: 3 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Data mining: Potential applications in research on nutrition and health. aug: au: Batterham, Marijka Neale, Elizabeth Martin, Allison Tapsell, Linda affil: Statistical Consulting Centre, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong New South Wales, Australia sug: subj: Data Mining Methods Weight Loss Obesity Therapy Forecasting Algorithms Software Funding Source Intervention Trials Evaluation Logistic Regression Neural Networks (Computer) Human Adipose Tissue Quality of Life Cholesterol Blood Lipoproteins, HDL Cholesterol Blood Physical Activity Blood Glucose Educational Status Pedometers Data Analysis Software Decision Trees Clinical Assessment Tools Adult Middle Age Descriptive Statistics Male Female P-Value Confidence Intervals Linear Regression Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Aim: Data mining enables further insights from nutrition ‐ related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition ‐ related intervention. Methods: Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. Results: The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables’ importance. CART found that body fat, quality of life using the SF ‐ 12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF ‐ 12PCS, glucose levels and level of education to be the most significant predictors (P ≤ 0.01). Conclusions: Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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