Volume 11, Issue 4 (Vol 11, No.4, Winter 2016 2016)                   irje 2016, 11(4): 46-54 | Back to browse issues page

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1- Assistant Professor, Department of Network Science and Technology, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran , mehditeimouri@ut.ac.ir
2- MSc student in Medical Information Technology, Department of Network Science and Technology, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
3- Assistant Professor, Vector-borne Diseases Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran
Abstract:   (9598 Views)

Background and Objectives: Diabetic patients are always at risk of hypertension. In this paper, the main goal was to design a native cost sensitive model for the diagnosis of hypertension among diabetics considering the prior probabilities.

Methods: In this paper, we tried to design a cost sensitive model for the diagnosis of hypertension in diabetic patients, considering the distribution of the disease in the general population. Among the data mining algorithms, Decision Tree, Artificial Neural Network, K-Nearest Neighbors, Support Vector Machine, and Logistic Regression were used. The data set belonged to Azarbayjan-e-Sharqi, Iran.

Results: For people with diabetes, a systolic blood pressure more than 130 mm Hg increased the risk of hypertension. In the non-cost-sensitive scenario, Youdenchr('39')s index was around 68%. On the other hand, in the cost-sensitive scenario, the highest Youdenchr('39')s index (47.11%) was for Neural Network. However, in the cost-sensitive scenario, the value of the imposed cost was important, and Decision Tree and Logistic Regression show better performances.

Conclusion: When diagnosing a disease, the cost of miss-classifications and also prior probabilities are the most important factors rather than only minimizing the error of classification on the data set.

Full-Text [PDF 1685 kb]   (2321 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/04/24 | Accepted: 2016/04/24 | Published: 2016/04/24