Classification of diagnostic subcategories for obesity and diabetes based on eating patterns.
Aim: To investigate whether eating patterns of specific food groups can be used to predict and classify Mexican adults who have been diagnosed as having obesity, diabetes or both, when compared to those without a diagnosis. Additionally, we aim to show the benefit of data mining techniques in nutrit...
| Published in: | Nutrition & Dietetics Vol. 76; no. 1; pp. 104 - 110 |
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| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
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Wiley-Blackwell
Feb2019
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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=134603291&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134603291 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14466368 QMK jtl: Nutrition & Dietetics issn: 14466368 maglogo: Y pubinfo: dt: Feb2019 vid: 76 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 134603291 134603291 134603291 10.1111/1747-0080.12495 134603291 ppf: 104 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of diagnostic subcategories for obesity and diabetes based on eating patterns. aug: au: Easton, Jonathan F. Román Sicilia, Heriberto Stephens, Christopher R. affil: Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México (UNAM), Mexico City Mexico sug: subj: Obesity Diagnosis Diabetes Mellitus Diagnosis Patient Classification Methods Eating Behavior Evaluation Human Female Adult Middle Age Mexico Models, Statistical Interviews T-Tests Confidence Intervals Data Mining Self Report Health Status Diet Food Habits Food Preferences Questionnaires Portion Size Health Behavior Funding Source Adult: 19-44 years Middle Aged: 45-64 years Female ab: Aim: To investigate whether eating patterns of specific food groups can be used to predict and classify Mexican adults who have been diagnosed as having obesity, diabetes or both, when compared to those without a diagnosis. Additionally, we aim to show the benefit of data mining techniques in nutritional studies. Methods: Statistical analysis of self‐reported eating patterns based on designated food groups is conducted. Predictive models for health status based on dietary patterns are built using a naïve Bayes classifier. Results: Clear patterns emerge in the model building where adults are categorised as having obesity, diabetes or both. The model for diabetics showed the greatest degree of predictability, producing sensitivity results 2.4 times higher than the average, using score decile testing. The models for people with obesity and for those with both obesity and diabetes both reported sensitivity doubling the average. Coverage also showed greatest response for the diabetic model, the first decile containing 24% of all diabetics. Conclusions: Classifier models using dietary habits as inputs succeed in subcategorising Mexican adults based on health status. Diabetics are associated with a very different, and more appropriate dietary pattern (significantly less sugar consumption) for their condition, relative to the non‐diagnosed group. Adults with obesity are also associated with a very different, but inappropriate (higher overall consumption), dietary pattern. We hypothesise that obesity, unlike diabetes, is not seen as a sufficiently serious condition, leading to an inadequate response to the diagnosis. Furthermore, data mining techniques can provide new results in nutritional studies. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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