Diabetes prediction based on the fusion of deep neural network, support vector machine and particle swarm optimization algorithm
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866902272640811008 |
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| author | Yu, Jing |
| author_facet | Yu, Jing |
| contents | <p><strong><span lang="EN-US">Figure. 1</span></strong> <span lang="EN-US">Content Framework Diagram of Model Construction</span><span lang="EN-US">.</span> <span lang="EN-US">[Utilizing the Kaggle diabetes dataset, high-dimensional features were extracted and optimally selected through deep neural networks, integrated with support vector machines for classification prediction, while employing particle swarm optimization algorithms for parameter tuning].</span></p> <p><span lang="EN-US"><strong><span lang="EN-US">Figure. 2 </span></strong><span lang="EN-US">Network Structure of the DNN Model.</span> <span lang="EN-US">[The DNN consists of an input layer, two hidden layers, and an output layer, with each layer comprising neurons equipped with activation functions. The dimensionality of the input layer is dictated by the dimension of the feature space, whereas the configuration of the output layer is determined by the specific requirements of the classification task].</span></span></p> <p><span lang="EN-US"><span lang="EN-US"><strong><span lang="EN-US">Figure. 3</span></strong><span lang="EN-US"> PSO training process.</span> <span lang="EN-US">[The PSO algorithm is integrated with the SVM model to automatically identify the optimal parameter configuration for the SVM]</span></span></span></p> <p><span lang="EN-US"><span lang="EN-US"><span lang="EN-US"><a name="OLE_LINK45"></a><a name="OLE_LINK27"></a><strong><span lang="EN-US">Figure</span></strong><strong><span lang="EN-US">.4</span></strong><span lang="EN-US"> Comparison of accuracy rates of each model</span><span lang="EN-US">.</span> <span lang="EN-US">[The bar chart visually compares the accuracy performance of various models on the identical dataset, with percentage values clearly demonstrating the performance disparities among the models]. </span></span></span></span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16986875 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Diabetes prediction based on the fusion of deep neural network, support vector machine and particle swarm optimization algorithm Yu, Jing <p><strong><span lang="EN-US">Figure. 1</span></strong> <span lang="EN-US">Content Framework Diagram of Model Construction</span><span lang="EN-US">.</span> <span lang="EN-US">[Utilizing the Kaggle diabetes dataset, high-dimensional features were extracted and optimally selected through deep neural networks, integrated with support vector machines for classification prediction, while employing particle swarm optimization algorithms for parameter tuning].</span></p> <p><span lang="EN-US"><strong><span lang="EN-US">Figure. 2 </span></strong><span lang="EN-US">Network Structure of the DNN Model.</span> <span lang="EN-US">[The DNN consists of an input layer, two hidden layers, and an output layer, with each layer comprising neurons equipped with activation functions. The dimensionality of the input layer is dictated by the dimension of the feature space, whereas the configuration of the output layer is determined by the specific requirements of the classification task].</span></span></p> <p><span lang="EN-US"><span lang="EN-US"><strong><span lang="EN-US">Figure. 3</span></strong><span lang="EN-US"> PSO training process.</span> <span lang="EN-US">[The PSO algorithm is integrated with the SVM model to automatically identify the optimal parameter configuration for the SVM]</span></span></span></p> <p><span lang="EN-US"><span lang="EN-US"><span lang="EN-US"><a name="OLE_LINK45"></a><a name="OLE_LINK27"></a><strong><span lang="EN-US">Figure</span></strong><strong><span lang="EN-US">.4</span></strong><span lang="EN-US"> Comparison of accuracy rates of each model</span><span lang="EN-US">.</span> <span lang="EN-US">[The bar chart visually compares the accuracy performance of various models on the identical dataset, with percentage values clearly demonstrating the performance disparities among the models]. </span></span></span></span></p> |
| title | Diabetes prediction based on the fusion of deep neural network, support vector machine and particle swarm optimization algorithm |
| url | https://doi.org/10.5281/zenodo.16986875 |