A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.
Background: We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. Study design: A cross-sectional study. Methods:...
| Publicado en: | Journal of Research in Health Sciences Vol. 18; no. 2; pp. 1 - 8 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Hamadan University of Medical Sciences, School of Public Health
Spring2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131316384&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131316384 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22287795 903Q jtl: Journal of Research in Health Sciences issn: 22287795 maglogo: N pubinfo: dt: Spring2018 vid: 18 iid: 2 pid: 54266 pub: Hamadan University of Medical Sciences, School of Public Health artinfo: ui: 131316384 131316384 131316384 131316384 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes. aug: au: Esmaily, Habibollah Tayefi, Maryam Doosti, Hassan Ghayour-Mobarhan, Majid Nezami, Hossein Amirabadizadeh, Alireza affil: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran sug: subj: Diabetes Mellitus, Type 2 Complications Decision Trees Data Mining Stroke Coronary Arteriosclerosis Risk Assessment Human Cross Sectional Studies Validity Anthropometry ROC Curve Health Policy Sensitivity and Specificity Prevalence ab: Background: We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. Study design: A cross-sectional study. Methods: The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. Results: The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. Conclusions: The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM . pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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