Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches
Stroke is a high morbidity and mortality disease that poses a serious threat to people’s health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representatio...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Stroke Research and Treatment |
| Online Access: | http://dx.doi.org/10.1155/2024/4523388 |
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| _version_ | 1850169359232663552 |
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| author | Chunhua Gao Hui Wang |
| author_facet | Chunhua Gao Hui Wang |
| author_sort | Chunhua Gao |
| collection | DOAJ |
| description | Stroke is a high morbidity and mortality disease that poses a serious threat to people’s health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN-GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning-based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. |
| format | Article |
| id | doaj-art-a83a6a9ccdb943d7aedbeff7c8185645 |
| institution | OA Journals |
| issn | 2042-0056 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stroke Research and Treatment |
| spelling | doaj-art-a83a6a9ccdb943d7aedbeff7c81856452025-08-20T02:20:44ZengWileyStroke Research and Treatment2042-00562024-01-01202410.1155/2024/4523388Intelligent Stroke Disease Prediction Model Using Deep Learning ApproachesChunhua Gao0Hui Wang1School of Tourism and Physical HealthSchool of Artificial IntelligenceStroke is a high morbidity and mortality disease that poses a serious threat to people’s health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN-GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning-based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases.http://dx.doi.org/10.1155/2024/4523388 |
| spellingShingle | Chunhua Gao Hui Wang Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches Stroke Research and Treatment |
| title | Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches |
| title_full | Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches |
| title_fullStr | Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches |
| title_full_unstemmed | Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches |
| title_short | Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches |
| title_sort | intelligent stroke disease prediction model using deep learning approaches |
| url | http://dx.doi.org/10.1155/2024/4523388 |
| work_keys_str_mv | AT chunhuagao intelligentstrokediseasepredictionmodelusingdeeplearningapproaches AT huiwang intelligentstrokediseasepredictionmodelusingdeeplearningapproaches |