Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2019.1622883
Prediction of stroke probability occurrence based on fuzzy cognitive maps
Mahsa Khodadadi
; Department of Control Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Heidarali Shayanfar
; Center of Excellence for Power Automation and Operation, College of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Keivan Maghooli
; Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Amir Hooshang Mazinan
; Department of Control Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Sažetak
Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%.
Ključne riječi
Fuzzy cognitive maps; ischemic; nonlinear Hebbian learning; prediction; stroke
Hrčak ID:
239824
URI
Datum izdavanja:
5.9.2019.
Posjeta: 593 *