Fuzzy cognitive map based approach for determining the risk of ischemic stroke. Issue 6 (1st December 2019)

• Record Type: Journal Article
• Authors: Khodadadi, Mahsa; Shayanfar, Heidarali; Maghooli, Keivan; Hooshang Mazinan, Amir
• Is Part Of: IET systems biology. Volume 13:Issue 6(2019)
• Page Start: 297
• Page End: 304
• Publication Date: 2019-12-01
• Subjects: patient diagnosis -- fuzzy logic -- diseases -- medical computing -- cognition -- learning (artificial intelligence) -- fuzzy set theory -- Hebbian learning -- neural nets -- support vector machines; ischemic stroke -- controllable factors -- uncontrollable factors -- blood pressure -- heart disease -- intelligent system -- immediate treatment -- soft computing method -- fuzzy cognitive mapping -- nonlinear Hebbian learning method -- fuzzy cognitive maps training -- risk rate; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2018.5128
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non‐linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10‐fold cross‐validation, for 110 real cases, and the results were compared with those of support vector machine and K ‐nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non‐commercial purposes.