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...
| Publicado en: | Stroke Research & Treatment Vol. 2024; pp. 1 - 11 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
5/23/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179684649&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179684649 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20908105 B6E2 jtl: Stroke Research & Treatment issn: 20908105 maglogo: N pubinfo: dt: 5/23/2024 vid: 2024 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 179684649 179684649 179684649 10.1155/2024/4523388 179684649 ppf: 1 ppct: 10 formats: tig: atl: Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches. aug: au: Gao, Chunhua Wang, Hui Mezzapesa, Domenico Maria affil: School of Tourism and Physical Health, Hezhou University, Hezhou 542899, China hzu.gx.cn sug: subj: Stroke Diagnosis Stroke Physiopathology Deep Learning Prediction Models Human Minimum Data Set Disease Attributes Stroke Risk Factors Risk Assessment Regression Comparative Studies Machine Learning Algorithms Decision Trees Random Forest Support Vector Machine Neural Networks (Computer) Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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