Volume 7, Issue 1 (5-2019)
jmsthums 2019, 7(1): 14-1 |
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I A, A A, H G, I Z. Diagnosis of diabetes by using a data mining method based on native data. jmsthums 2019; 7 (1) :14-1
URL: http://jms.thums.ac.ir/article-1-546-en.html
URL: http://jms.thums.ac.ir/article-1-546-en.html
1- Department of Computer Engineering, Islamic Azad University, Ferdows Branch,Ferdows, Iran
2- Department of Computer Egineering, Islamic Azad University, Torbat Heydariyeh Branch, Torbat Heydariyeh, Iran
2- Department of Computer Egineering, Islamic Azad University, Torbat Heydariyeh Branch, Torbat Heydariyeh, Iran
Abstract: (5645 Views)
Background & Aim: Detecting the abnormal performance of diabetes and subsequently getting proper treatment can reduce the mortality associated with the disease. Also, timely diagnosis will result in irreversible complications for the patient. The aim of this study was to determine the status of diabetes mellitus using data mining techniques.
Methods: This is an analytical study and its database contains 254 independent records based on 13 characteristics. Data is collected by a researcher from one of the specialized diabetes centers in Mashhad.
Results: After preprocessing of the obtained data, different methods of pattern recognition were applied. Using multilevel MLP neural networks, LVQ neural networks, SVM support vector and Kmeans clustering method, the mean square error (RMSE) was calculated. The correctness of each learner's performance is 94%, 92%, 96%, and 93%, respectively.
Conclusion: Reducing the diagnosis of diabetes is one of the goals of the researchers. Using data mining techniques can help to reduce this error. In this study, among different protocols used for pattern recognition, SMV method displayed a significantly better performance.
Methods: This is an analytical study and its database contains 254 independent records based on 13 characteristics. Data is collected by a researcher from one of the specialized diabetes centers in Mashhad.
Results: After preprocessing of the obtained data, different methods of pattern recognition were applied. Using multilevel MLP neural networks, LVQ neural networks, SVM support vector and Kmeans clustering method, the mean square error (RMSE) was calculated. The correctness of each learner's performance is 94%, 92%, 96%, and 93%, respectively.
Conclusion: Reducing the diagnosis of diabetes is one of the goals of the researchers. Using data mining techniques can help to reduce this error. In this study, among different protocols used for pattern recognition, SMV method displayed a significantly better performance.
Type of Study: Research |
Subject:
Special
Received: 2018/11/13 | Accepted: 2019/03/9 | Published: 2019/09/14
Received: 2018/11/13 | Accepted: 2019/03/9 | Published: 2019/09/14
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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