Volume 16, Number 53 (10-2013)                   jha 2013, 16(53): 58-72 | Back to browse issues page


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Ameri H, Alizadeh S, Barzegari A. Knowledge Extraction of Diabetics' Data by Decision Tree Method. jha. 2013; 16 (53) :58-72
URL: http://jha.iums.ac.ir/article-1-1351-en.html

1- MSc in E-Commerce, Information Technology Department, Faculty of Industrial Engineering, KN Toosi University of Technology, Tehran, Iran KNTU university
2- Assistant Professor of Information Technology Department, Faculty of Industrial Engineering, KN Toosi University of Technology, Tehran, Iran KNTU University , hameri@mail.kntu.ac.ir
3- General Physician, Golestan University of Medical Science, Gorgan, Iran Golestan University of Medical Sciences
Abstract:   (5533 Views)
Introduction: In the last 10 years The incidence of diabetes has doubled worldwide with annual increasing rate of about 6%. More than 2 million people in Iran are now affected by this disease. The present research deals with the relation between the observed complications of type 2 diabetic patients and some related features like Blood Glucose Level, Blood Pressure, Age, and Family History. The main purpose was to predict the patients’ complications based on the observed signs.
Methods:
The research data were gathered from 856 patient records related to the 2009’s cases in the Diabetes Center of Golestan province. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called C5.0 decision tree and Neural Network were used. Celementine 12.0 software was implemented For data analysis.
Results:
The results of data mining showed that the variables of high blood pressure, age, and family history had the most impact on the observed complications. Based on the created decision tree, some rules have been extracted which can be used as a pattern to predict the probability of occurring these complications in the patients. The accuracy of the C5.0 model on the data was shown to be 89.74% and on the Artificial Neural Network was 51.28%.
Conclusion: As the highest accuracy was shown to be achieved using C5.0 algorithm, according to the created rules, it can be predicted which complication(s) any diabetic patient with new specified features may probably suffer from.
Full-Text [PDF 503 kb]   (4943 Downloads)    
Type of Study: Research | Subject: General
Received: 2013/06/9 | Accepted: 2013/11/19 | Published: 2013/11/19

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