Isolation forest-voting fusion-multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection-Reference-Cited by-同舟云学术

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Isolation forest-voting fusion-multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection

Published:2024-08 Issue: Volume:253 Page:108255
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

He Hai^ORCID,Yang Haibo^ORCID,Mercaldo Francesco,Santone Antonella^ORCID,Huang Pan

Funder

Chongqing Municipal Education Commission

Publisher

Elsevier BV

Reference40 articles.

1. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review;Feigin;Lancet Neurol.,2009

2. Atlas of the Global Burden of Stroke (1990-2013): the GBD 2013 Study;Feigin;Neuroepidemiology,2015

3. Health System Reform in China 3 Emergence of chronic non-communicable diseases in China;Yang;Lancet,2008

4. Machine learning provides evidence that stroke risk is not linear: the non-linear Framingham stroke risk score;Orfanoudaki;PLoS One,2020

5. Predicting Functional Outcome Based on Linked Data After Acute Ischemic Stroke: S-SMART Score;Kim;Transl Stroke Res,2020

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