Tree-Based Risk Factor Identification and Stroke Level Prediction in Stroke Cohort Study.
Objective. This study focuses on the identification of risk factors, classification of stroke level, and evaluation of the importance and interactions of various patient characteristics using cohort data from the Second Hospital of Lanzhou University. Methodology. Risk factors are identified by eval...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/10/2023
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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=163024424&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163024424 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/10/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 163024424 163024424 163024424 10.1155/2023/7352191 163024424 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Tree-Based Risk Factor Identification and Stroke Level Prediction in Stroke Cohort Study. aug: au: Li, Junyao Luo, Yuxiang Dong, Meina Liang, Yating Zhao, Xuejing Zhang, Yafeng Ge, Zhaoming affil: School of Mathematics and Statistics, Center for Data Science, Lanzhou University, Lanzhou, 730000, China sug: subj: Risk Assessment Stroke Risk Factors Stroke Classification Disease Attributes Human China Hospitals Hypertension Stroke Diagnosis Age Factors Sex Factors Cerebral Ischemia, Transient Family History Funding Source Obesity Hyperlipidemia Smoking Prospective Studies ab: Objective. This study focuses on the identification of risk factors, classification of stroke level, and evaluation of the importance and interactions of various patient characteristics using cohort data from the Second Hospital of Lanzhou University. Methodology. Risk factors are identified by evaluation of the relationships between factors and response, as well as by ranking the importance of characteristics. Then, after discarding negligible factors, some well-known multicategorical classification algorithms are used to predict the level of stroke. In addition, using the Shapley additive explanation method (SHAP), factors with positive and negative effects are identified, and some important interactions for classifying the level of stroke are proposed. A waterfall plot for a specific patient is presented and used to determine the risk degree of that patient. Results and Conclusion. The results show that (1) the most important risk factors for stroke are hypertension, history of transient ischemia, and history of stroke; age and gender have a negligible impact. (2) The XGBoost model shows the best performance in predicting stroke risk; it also gives a ranking of risk factors based on their impact. (3) A combination of SHAP and XGBoost can be used to identify positive and negative factors and their interactions in stroke prediction, thereby providing helpful guidance for diagnosis. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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