Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm
Data mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigatin...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2019-11-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_1567_33c48b430329aa2def551f03f3f076c9.pdf |
| _version_ | 1818499720892383232 |
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| author | S. Taherian Dehkordi A. Khatibi Bardsiri M. H. Zahedi |
| author_facet | S. Taherian Dehkordi A. Khatibi Bardsiri M. H. Zahedi |
| author_sort | S. Taherian Dehkordi |
| collection | DOAJ |
| description | Data mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigating patients' records. Early diagnosis of diabetes can reduce the effects of this devastating disease. A common way to diagnose this disease is performing a blood test, which, despite its high precision, has some disadvantages such as: pain, cost, patient stress, lack of access to a laboratory, and so on. Diabetic patients’ information has hidden patterns, which can help you investigate the risk of diabetes in individuals, without performing any blood tests. Use of neural networks, as powerful data mining tools, is an appropriate method to discover hidden patterns in diabetic patients’ information. In this paper, in order to discover the hidden patterns and diagnose diabetes, a water wave optimization(WWO) algorithm; as a precise metaheuristic algorithm, was used along with a neural network to increase the precision of diabetes prediction. The results of our implementation in the MATLAB programming environment, using the dataset related to diabetes, indicated that the proposed method diagnosed diabetes at a precision of 94.73%,sensitivity of 94.20%, specificity of 93.34%, and accuracy of 95.46%, and was more sensitive than methods such as: support vector machines, artificial neural networks, and decision trees. |
| first_indexed | 2024-12-10T20:33:17Z |
| format | Article |
| id | doaj.art-2cb40d00985143a2abf63bb934d6a92a |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-10T20:33:17Z |
| publishDate | 2019-11-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-2cb40d00985143a2abf63bb934d6a92a2022-12-22T01:34:36ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442019-11-017461763010.22044/jadm.2018.6446.17581567Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization AlgorithmS. Taherian Dehkordi0A. Khatibi Bardsiri1M. H. Zahedi2Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran.Department of Computer Engineering, Bardsir Branch, Islamic Azad University, Bardsir, Iran.Faculty of Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology, Tehran, IranData mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigating patients' records. Early diagnosis of diabetes can reduce the effects of this devastating disease. A common way to diagnose this disease is performing a blood test, which, despite its high precision, has some disadvantages such as: pain, cost, patient stress, lack of access to a laboratory, and so on. Diabetic patients’ information has hidden patterns, which can help you investigate the risk of diabetes in individuals, without performing any blood tests. Use of neural networks, as powerful data mining tools, is an appropriate method to discover hidden patterns in diabetic patients’ information. In this paper, in order to discover the hidden patterns and diagnose diabetes, a water wave optimization(WWO) algorithm; as a precise metaheuristic algorithm, was used along with a neural network to increase the precision of diabetes prediction. The results of our implementation in the MATLAB programming environment, using the dataset related to diabetes, indicated that the proposed method diagnosed diabetes at a precision of 94.73%,sensitivity of 94.20%, specificity of 93.34%, and accuracy of 95.46%, and was more sensitive than methods such as: support vector machines, artificial neural networks, and decision trees.http://jad.shahroodut.ac.ir/article_1567_33c48b430329aa2def551f03f3f076c9.pdfdiabetes mellitusdata miningartificial neural networkswater wave optimization (wwo) algorithm |
| spellingShingle | S. Taherian Dehkordi A. Khatibi Bardsiri M. H. Zahedi Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm Journal of Artificial Intelligence and Data Mining diabetes mellitus data mining artificial neural networks water wave optimization (wwo) algorithm |
| title | Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm |
| title_full | Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm |
| title_fullStr | Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm |
| title_full_unstemmed | Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm |
| title_short | Prediction and Diagnosis of Diabetes Mellitus using a Water Wave Optimization Algorithm |
| title_sort | prediction and diagnosis of diabetes mellitus using a water wave optimization algorithm |
| topic | diabetes mellitus data mining artificial neural networks water wave optimization (wwo) algorithm |
| url | http://jad.shahroodut.ac.ir/article_1567_33c48b430329aa2def551f03f3f076c9.pdf |
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