Volume 6, Issue 2 (9-2018)
jmsthums 2018, 6(2): 10-20 |
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Zabbah I, Eskandari A, Sardari Z, Noghandi A. Diagnosis of Diabetes using Artificial Neural Network and Neuro-Fuzzy approach. jmsthums 2018; 6 (2) :10-20
URL: http://jms.thums.ac.ir/article-1-500-en.html
URL: http://jms.thums.ac.ir/article-1-500-en.html
1- Departement fo Electrical and Computer Engineering, University of Torbat Heydarieh, Iran
2- University of Torbat Heydarieh, Torbat Heydarieh, Iran
2- University of Torbat Heydarieh, Torbat Heydarieh, Iran
Abstract: (5683 Views)
Background & Aim: A main problem in diabetes is its timely and accurate diagnosis. This study aimed at diagnosing diabetes using data mining methods.
Methods: The present study is an analytical investigation including 768 individuals with 8 attributes. Artificial neural networks and fuzzy neural networks were used to diagnose the diabetes. To achieve a real accuracy, the Kfold method was used to divide samples into training and test groups.
Results: The mean square errors in multilayer perceptron network (MLP), learning vector quantization and Nero fuzzy networks were 98.6%, 98.2% and 99.6%, respectively.
Conclusion: According to the results of this study, , data mining method can be effective in diagnosing diabetes. In this regard, both used methods are useful; however, higher precision was obtained following the use of Neuro-Fuzzy approach.
Methods: The present study is an analytical investigation including 768 individuals with 8 attributes. Artificial neural networks and fuzzy neural networks were used to diagnose the diabetes. To achieve a real accuracy, the Kfold method was used to divide samples into training and test groups.
Results: The mean square errors in multilayer perceptron network (MLP), learning vector quantization and Nero fuzzy networks were 98.6%, 98.2% and 99.6%, respectively.
Conclusion: According to the results of this study, , data mining method can be effective in diagnosing diabetes. In this regard, both used methods are useful; however, higher precision was obtained following the use of Neuro-Fuzzy approach.
Type of Study: Research |
Subject:
General
Received: 2018/07/12 | Accepted: 2018/10/10 | Published: 2019/01/23
Received: 2018/07/12 | Accepted: 2018/10/10 | Published: 2019/01/23
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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