Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling.
Background: Type 2 Diabetes Mellitus (T2DM) is one of the most important risk factors in cardiovascular disorders considered as a common clinical and public health problem. Early diagnosis can reduce the burden of the disease. Decision tree, as an advanced data mining method, can be used as a reliab...
| Publicado en: | International Cardiovascular Research Journal Vol. 11; no. 2; pp. 71 - 77 |
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| Autores principales: | , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Brieflands
Jun2017
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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=123817077&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123817077 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22519130 FBPD jtl: International Cardiovascular Research Journal issn: 22519130 maglogo: N pubinfo: dt: Jun2017 vid: 11 iid: 2 pid: 69510 pub: Brieflands place: , <Blank> artinfo: ui: 123817077 123817077 123817077 123817077 ppf: 71 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling. aug: au: Sayadi, Mehrab Zibaeenezhad, Mohammadjavad Taghi Ayatollahi, Seyyed Mohammad affil: Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, IR Iran sug: subj: Diabetes Mellitus, Type 2 Diagnosis Decision Trees Utilization Human Iran Adolescence Adult Middle Age Aged Aged, 80 and Over Male Female Age Factors Models, Statistical Decision Support Techniques Health Screening Public Health Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: Type 2 Diabetes Mellitus (T2DM) is one of the most important risk factors in cardiovascular disorders considered as a common clinical and public health problem. Early diagnosis can reduce the burden of the disease. Decision tree, as an advanced data mining method, can be used as a reliable tool to predict T2DM. Objectives: This study aimed to present a simple model for predicting T2DM using decision tree modeling. Materials and Methods: This analytical model-based study used a part of the cohort data obtained from a database in Healthy Heart House of Shiraz, Iran. The data included routine information, such as age, gender, Body Mass Index (BMI), family history of diabetes, and systolic and diastolic blood pressure, which were obtained from the individuals referred for gathering baseline data in Shiraz cohort study from 2014 to 2015. Diabetes diagnosis was used as binary datum. Decision tree technique and J48 algorithm were applied using the WEKA software (version 3.7.5, New Zealand). Additionally, Receiver Operator Characteristic (ROC) curve and Area Under Curve (AUC) were used for checking the goodness of fit. Results: The age of the 11302 cases obtained after data preparation ranged from 18 to 89 years with the mean age of 48.1 ± 11.4 years. Additionally, 51.1% of the cases were male. In the tree structure, blood pressure and age were placed where most information was gained. In our model, however, gender was not important and was placed on the final branch of the tree. Total precision and AUC were 87% and 89%, respectively. This indicated that the model had good accuracy for distinguishing patients from normal individuals. Conclusions: The results showed that T2DM could be predicted via decision tree model without laboratory tests. Thus, this model can be used in pre-clinical and public health screening programs. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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