Volume 18, Issue 2 (2-2019)
ijdld 2019, 18(2): 71-79 |
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Khosravanian A, Ayat S.
A PHYSICIAN ASSISTANT INTELLIGENCE SYSTEM BASED ON ARTIFICIAL NEURAL NETWORK FOR DIABETES DIAGNOSIS
. ijdld 2019; 18 (2) :71-79
URL: http://ijdld.tums.ac.ir/article-1-5744-en.html
URL: http://ijdld.tums.ac.ir/article-1-5744-en.html
1- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
2- Department of Computer Engineering and Information Technology, Payame Noor University, Iran ,dr.ayat@pnu.ac.ir
2- Department of Computer Engineering and Information Technology, Payame Noor University, Iran ,
Abstract: (3394 Views)
Backgrounds: Early detection of diabetes is critical to avoid complications and damage caused by this disease. The purpose of this paper is designing an intelligent system for Diabetes prediction (healthy or patient) by using regression method based on Multilayer Perceptron Neural Network.
Methods: In this descriptive-analytic study, an intelligent system is designed to classification diabetes patients. The system is simulated by MATLAB software 2015 (8.5.0.197613). In this study, used PID dataset in UCI Machine Learning Repository. The dataset is contained 768 records from Indian women and 8 diagnostic factors for Diabetes.
Results: The data were then divided randomly in 20 groups for training and testing, after preprocessing. 90% of the data is used for training phase and 10% for the test phase. The results obtained based on sensitivity, specificity, accuracy and precision were 0.4815, 0.9804, 0.8077 and 0.9286, respectively.
Conclusion: The obtained results, showed superiority of designed intelligent system to classify individuals (healthy and patient) in comparison with other methods implemented on this dataset. Using MLP- Regression has increased the accuracy of the proposed system.
Methods: In this descriptive-analytic study, an intelligent system is designed to classification diabetes patients. The system is simulated by MATLAB software 2015 (8.5.0.197613). In this study, used PID dataset in UCI Machine Learning Repository. The dataset is contained 768 records from Indian women and 8 diagnostic factors for Diabetes.
Results: The data were then divided randomly in 20 groups for training and testing, after preprocessing. 90% of the data is used for training phase and 10% for the test phase. The results obtained based on sensitivity, specificity, accuracy and precision were 0.4815, 0.9804, 0.8077 and 0.9286, respectively.
Conclusion: The obtained results, showed superiority of designed intelligent system to classify individuals (healthy and patient) in comparison with other methods implemented on this dataset. Using MLP- Regression has increased the accuracy of the proposed system.
Type of Study: Research |
Subject:
Special
Received: 2018/08/20 | Accepted: 2019/01/5 | Published: 2019/02/15
Received: 2018/08/20 | Accepted: 2019/01/5 | Published: 2019/02/15
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