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AuthorAli, Rahma
AuthorQidwai, Uvais
AuthorIlyas, Saadat K.
AuthorAkhtar, Naveed
AuthorAlboudi, Ayman
AuthorAhmed, Arsalan
AuthorInshasi, Jihad
Available date2020-08-18T08:34:45Z
Publication Date2019
Publication NameInternational Journal of Integrated Engineering
ResourceScopus
ISSN2229838X
URIhttp://dx.doi.org/10.30880/ijie.2019.11.03.007
URIhttp://hdl.handle.net/10576/15663
AbstractWith the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predictsthesecondinfarctionvolumegrowthrateisadoptedfromanearliermodel,weproposethelatermodelin this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic. - Universiti Tun Hussein Onn M alay sia Publisher's Office.
Languageen
PublisherPenerbit UTHM
SubjectArtificial Neural Network
Infarct Growth Rate
Infarction Volume
Ischemic Stroke
Neuro-Fuzzy Inference System
Stroke
Support Vector Machine
TitleAdaptive neuro-fuzzy inference system for prediction of surgery time for ischemic stroke patients
TypeArticle
Pagination60-69
Issue Number3
Volume Number11
dc.accessType Abstract Only


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