Fuzzy cognitive map based approach for determining the risk of ischemic stroke
Mahsa Khodadadi1, Heidarali Shayanfar2, Keivan Maghooli3
1Department of Control Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Insights
This study introduces a fuzzy cognitive mapping system to diagnose ischemic stroke risk, achieving 93.6% accuracy. This intelligent system aids in early detection and effective treatment planning for stroke patients.
Area of Science:
- Neurology
- Artificial Intelligence
- Computational Medicine
Background:
- Stroke is a leading global cause of mortality, with diagnosis complicated by numerous risk factors.
- Effective and immediate stroke diagnosis is crucial for timely treatment and improved patient outcomes.
- Intelligent systems offer potential for enhancing stroke risk assessment and diagnosis.
Purpose of the Study:
- To propose and evaluate a soft computing method, fuzzy cognitive mapping (FCM), for diagnosing ischemic stroke risk.
- To assess the performance of the FCM model against established machine learning algorithms.
- To provide a reliable tool for identifying individuals at high risk of ischemic stroke.
Main Methods:
- Fuzzy cognitive mapping (FCM) was employed for ischemic stroke risk diagnosis.
- Non-linear Hebbian learning was utilized for training the fuzzy cognitive maps.
- The system's risk rate determination was based on expert neurologist opinions.
- Model performance was validated using 110 real cases with 10-fold cross-validation.
Main Results:
- The proposed FCM system demonstrated superior performance compared to Support Vector Machine (SVM) and K-Nearest Neighbors (KNN).
- The system achieved a high accuracy rate of (93.6 ± 4.5)%.
- The results indicate the effectiveness of FCM in stroke risk diagnosis.
Conclusions:
- Fuzzy cognitive mapping, trained with non-linear Hebbian learning, is a highly accurate method for ischemic stroke risk diagnosis.
- The developed intelligent system shows significant potential for clinical application in stroke prevention and management.
- The study highlights the utility of soft computing approaches in complex medical diagnostic challenges.
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.
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