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Information Journal Paper

Title

Diagnosis of Diabetes using Artificial Neural Network and Neuro-Fuzzy approach

Pages

  10-20

Abstract

 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.

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    APA: Copy

    ZABBAH, IMAN, Eskandari, Asma, Sardari, Zahra, & Noghandi, Abolfazl. (2018). Diagnosis of Diabetes using Artificial Neural Network and Neuro-Fuzzy approach. JOURNAL OF TORBAT HEYDARIYEH UNIVERSITY OF MEDICAL SCIENCES, 6(2 ), 10-20. SID. https://sid.ir/paper/244707/en

    Vancouver: Copy

    ZABBAH IMAN, Eskandari Asma, Sardari Zahra, Noghandi Abolfazl. Diagnosis of Diabetes using Artificial Neural Network and Neuro-Fuzzy approach. JOURNAL OF TORBAT HEYDARIYEH UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2018;6(2 ):10-20. Available from: https://sid.ir/paper/244707/en

    IEEE: Copy

    IMAN ZABBAH, Asma Eskandari, Zahra Sardari, and Abolfazl Noghandi, “Diagnosis of Diabetes using Artificial Neural Network and Neuro-Fuzzy approach,” JOURNAL OF TORBAT HEYDARIYEH UNIVERSITY OF MEDICAL SCIENCES, vol. 6, no. 2 , pp. 10–20, 2018, [Online]. Available: https://sid.ir/paper/244707/en

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