Applying decision tree for detection of a low risk population for type 2 diabetes: A population based study

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Habibollah Esmaeily
Maryam Tayefi
Hassan Doosti
Majid Ghayour-Mobarhan
Ali Reza Amirabadizadeh

Abstract

Introduction: The aim of current study was to create a prediction model using data mining approach, decision tree technique, to identify low risk individuals for incidence of Type 2 diabetes (T2DM), using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program.


Methods: a prediction model was developed using classification by the decision tree method on 9528 subjects recruited from MASHAD database. Moreover, the receiver operating characteristic (ROC) curve was applied.


Results: The prevalence rate of T2DM was ~14% in our population. For decision tree model, the accuracy, sensitivity, and specificity value for identifying the related factors with T2DM were 78.7%, 47.8% and 83%, respectively. In addition, the area under the ROC curve (AUC) value for recognizing the risk factors associated with T2DM was 0.64. Moreover, we found that subjects with family history of T2DM, age>=48, SBP>=130, DBP>=81, HDL>=29, LDL>=148 and occupation=other have more than 59% chance of this disorder, while the chance of T2DM in subjects without history with TG>=184, age>=48 and hs-CRP>=2.2, have approximately 51% chance.


Conclusion: Our findings demonstrated that decision tree analysis, using routine demographic, clinical, anthropometric and biochemical measurements, which combined with other risk score models, could create a simple strategy to predict individuals at low risk for type 2 diabetes in order to decrease substantially the number of subjects needing for screening and recognition of subject at high risk.

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How to Cite
Applying decision tree for detection of a low risk population for type 2 diabetes: A population based study. (2017). Medical Technologies Journal, 1(4), 132-132. https://doi.org/10.26415/2572-004X-vol1iss4p132-132
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Conference abstracts

How to Cite

Applying decision tree for detection of a low risk population for type 2 diabetes: A population based study. (2017). Medical Technologies Journal, 1(4), 132-132. https://doi.org/10.26415/2572-004X-vol1iss4p132-132

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