Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes
Data mining is an appropriate approach for uncovering information and hidden patterns within extensive datasets that are not readily detectable through conventional methodologies. This method has wide applications in various sciences, and one of its interesting applications is to identify diseases a...
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Bilijipub publisher
2024-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_193334_4f8796ade1336af1f21875442329d7e8.pdf |
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| author | Cillian Thompson Oscar Higgins |
| author_facet | Cillian Thompson Oscar Higgins |
| author_sort | Cillian Thompson |
| collection | DOAJ |
| description | Data mining is an appropriate approach for uncovering information and hidden patterns within extensive datasets that are not readily detectable through conventional methodologies. This method has wide applications in various sciences, and one of its interesting applications is to identify diseases and disease patterns by examining patients' medical records. Diabetes is one of the challenges of today's society, which is influenced by important factors such as nutrition, obesity, physical inactivity, and genetic background. Early diagnosis of diabetes reduces the effects of this destructive disease. The usual method for diagnosing this disease is to perform a blood test, which, despite its high accuracy, has disadvantages such as pain, cost, stress, and limited availability of laboratory facilities. The information of diabetic patients has hidden patterns that can be used to check the possibility of diabetes in people. As a powerful data mining tool, neural networks are a suitable method for discovering hidden patterns in the information of diabetic patients. In this study, in order to discover hidden patterns and diagnose diabetes, a particle swarm intelligence algorithm has been used along with a neural network to increase the accuracy of diabetes diagnosis. The general results of the research showed that the proposed method has accuracy, specificity and sensitivity of about 94.15%, 92.89% and 92.13%, respectively. Furthermore, in diabetic disease modelling, artificial neural networks have demonstrated outstanding accuracy compared to alternative methods such as machine learning, regression, artificial neural networks, and decision trees. |
| format | Article |
| id | doaj-art-497b02ad9c2f4239bed97dee759d0004 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-497b02ad9c2f4239bed97dee759d00042025-02-12T08:47:46ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-03-0100301233310.22034/aeis.2023.427439.1146193334Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing DiabetesCillian Thompson0Oscar Higgins1School of Information and Communication Studies, University College Dublin, Dublin, IrelandDepartment of Mechanical Engineering, School of Engineering, College of Science and Engineering, National University of Ireland Galway, Galway, IrelandData mining is an appropriate approach for uncovering information and hidden patterns within extensive datasets that are not readily detectable through conventional methodologies. This method has wide applications in various sciences, and one of its interesting applications is to identify diseases and disease patterns by examining patients' medical records. Diabetes is one of the challenges of today's society, which is influenced by important factors such as nutrition, obesity, physical inactivity, and genetic background. Early diagnosis of diabetes reduces the effects of this destructive disease. The usual method for diagnosing this disease is to perform a blood test, which, despite its high accuracy, has disadvantages such as pain, cost, stress, and limited availability of laboratory facilities. The information of diabetic patients has hidden patterns that can be used to check the possibility of diabetes in people. As a powerful data mining tool, neural networks are a suitable method for discovering hidden patterns in the information of diabetic patients. In this study, in order to discover hidden patterns and diagnose diabetes, a particle swarm intelligence algorithm has been used along with a neural network to increase the accuracy of diabetes diagnosis. The general results of the research showed that the proposed method has accuracy, specificity and sensitivity of about 94.15%, 92.89% and 92.13%, respectively. Furthermore, in diabetic disease modelling, artificial neural networks have demonstrated outstanding accuracy compared to alternative methods such as machine learning, regression, artificial neural networks, and decision trees.https://aeis.bilijipub.com/article_193334_4f8796ade1336af1f21875442329d7e8.pdfdiabetesdata miningneural networkparticle swarm intelligencepattern recognition |
| spellingShingle | Cillian Thompson Oscar Higgins Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes Advances in Engineering and Intelligence Systems diabetes data mining neural network particle swarm intelligence pattern recognition |
| title | Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes |
| title_full | Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes |
| title_fullStr | Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes |
| title_full_unstemmed | Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes |
| title_short | Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes |
| title_sort | combination of artificial neural network and particle swarm intelligence algorithm for diagnosing diabetes |
| topic | diabetes data mining neural network particle swarm intelligence pattern recognition |
| url | https://aeis.bilijipub.com/article_193334_4f8796ade1336af1f21875442329d7e8.pdf |
| work_keys_str_mv | AT cillianthompson combinationofartificialneuralnetworkandparticleswarmintelligencealgorithmfordiagnosingdiabetes AT oscarhiggins combinationofartificialneuralnetworkandparticleswarmintelligencealgorithmfordiagnosingdiabetes |