Volume 15, Number 3 (3-2017)                   ijdld 2017, 15(3): 172-176 | Back to browse issues page


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Shirali M, Madmoli Y, Roohafza J, Karimi H, Baboli Bahmaei A, Ertebati S. IMPROVEMENT DIAGNOSIS OF DIABETES USING A COMBINATION OF SUGENO FUZZY INFERENCE SYSTEMS AND FIREFLY ALGORITHMS. ijdld. 2017; 15 (3) :172-176
URL: http://ijdld.tums.ac.ir/article-1-5344-en.html

1- Student in Electrical Engineering, Bushehr Islamic Azad University, Bushehr, Iran
2- Student Research Committee, Dezful University of Medical Sciences, Dezful, Iran
3- Student Research Committee, Dezful University of Medical Sciences, Dezful, Iran , roohafzaj@gmail.com
4- Subspecialist of Endocrine, Growth and Metabolism, Faculty member of Dezful University of Medical Sciences, Dezful, Iran
Abstract:   (3083 Views)

Background: Today many people are likely to have diabetes. Diabetes is a serious disease of modern societies and its on-time diagnosis have important role in the treatment of disease. Methods: In this paper, using Sugeno fuzzy inference systems and intelligent algorithms Firefly, a new method is presented for the detection of diabetes. The proposed method enables the use of a few simple fuzzy rules to detect diabetes with good accuracy. Results: Sugeno fuzzy inference system and firefly algorithm com

Background: Today many people are likely to have diabetes. Diabetes is a serious disease of modern societies and its on-time diagnosis have important role in the treatment of disease.

Methods: In this paper, using Sugeno fuzzy inference systems and intelligent algorithms Firefly, a new method is presented for the detection of diabetes. The proposed method enables the use of a few simple fuzzy rules to detect diabetes with good accuracy.

Results: Sugeno fuzzy inference system and firefly algorithm combines efficiency was 87.24 percent.

Conclusion: Experimental results show that this method deals on the data set is more accurate than the standard PID algorithm in this field.

bines efficiency was 87.24 percent. Conclusion: Experimental results show that this method deals on the data set is more accurate than the standard PID algorithm in this field.

Full-Text [PDF 289 kb]   (1135 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2015/05/25 | Accepted: 2015/09/8 | Published: 2016/04/20

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