APPLYING DECISION TREE FOR DETECTION OF A RISK FACTORS FOR TYPE 2 DIABETES: A POPULATION BASED STUDY

Abstract

Abstract

Introduction: The aim of current study was to create a prediction model using a data mining approach and 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 the MASHAD database. Moreover, the receiver operating characteristic (ROC) curve was applied.

Results: The prevalence rate of T2DM was 14% in our population. For the decision tree model, the accuracy, sensitivity, and specificity value for identifying the related factors with T2DM were 78.7%, 61.2%, and 83%, respectively. In addition, the area under the ROC curve (AUC) value for recognizing the risk factors associated with T2DM was 68%. The identified variables included family history of diabetes, triglycerides, systolic blood pressure, body mass index, hs-crp, education. 

Conclusion: Our findings demonstrated that decision tree analysis, using routine demographic, clinical, and 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 substantially decrease the number of subjects needed for screening and recognition of subjects at high risk.