Assessment of stroke risk using soft computing

Subject Areas : Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

Mahsa Khodadadi ^{1 *}

1 - Department of Electrical Engineering, Bon.C., Islamic Azad University, Bonab, Iran

Received: 2025-11-08 Accepted : 2025-12-29 Published : 2026-01-06

Keywords: Assessment, Biomedical engineering, Fuzzy cognitive maps, Ischemic, Risk, Stroke,

Abstract :

Stroke risk assessment is complex in biomedical engineering due to the interaction of multiple clinical and lifestyle-related risk factors, which may not be consistently or comprehensively evaluated in routine clinical practice. To address this challenge, this study proposes a non-learning soft computing framework based on fuzzy cognitive mapping for stroke risk assessment. The proposed model integrates expert knowledge from three neurologists to construct the fuzzy cognitive map and assigns individual risk levels into low, moderate, and high categories. Model performance was evaluated using 10-fold cross-validation on a dataset of 110 individuals and benchmarked against the fuzzy c-means clustering algorithm and logistic regression. Experimental results demonstrate that the proposed FCM-based system outperforms the comparative methods, achieving an overall classification accuracy of 90.7%. These findings indicate that the proposed approach provides an interpretable and effective decision-support tool for stroke risk assessment.

References:


[1] Khodadadi, M., Shayanfar, H., Maghooli, K., & Hooshang Mazinan, A. (2019). Prediction of stroke probability occurrence based on fuzzy cognitive map. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 60 (4), 378-385.
[2] Apostolopoulos, I. D., & Groumpos, P. P. (2023). Fuzzy cognitive maps: Their role in explainable artificial intelligence. Applied Sciences, 13(6), 3412.
[3] Amirkhani, A., Papageorgiou, E. I., Mohseni, A., & Mosavi, M. R. (2017). A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. Computer methods and programs in biomedicine, 142, 129-145.
[4] Subramanian, J., Karmegam, A., Papageorgiou, E., Papandrianos, N., & Vasukie, A. (2015). An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. Computer methods and programs in biomedicine, 118(3), 280-297.
[5] Bautista, J. A. R., Chaparro-Cárdenas, S. L., Hernández-Zavala, A., Gallegos-Torres, R. M., Zequera, M., Tovar-Barrera, Y., ... & Huerta-Ruelas, J. A. (2022). Fuzzy cognitive map to classify plantar foot alterations. IEEE Latin America Transactions, 20(7), 1092-2000.
[6] Orang, O., de Lima e Silva, P. C., & Guimarães, F. G. (2023). Time series forecasting using fuzzy cognitive maps: a survey. Artificial Intelligence Review, 56(8), 7733-7794.
[7] Akinnuwesi, B. A., Adegbite, B. A., Adelowo, F., Ima-Edomwonyi, U., Fashoto, G., & Amumeji, O. T. (2020). Decision support system for diagnosing rheumatic-musculoskeletal disease using fuzzy cognitive map technique. Informatics in medicine unlocked, 18, 100279.
[8] Khodadadi, M., Shayanfar, H., Maghooli, K., & Hooshang Mazinan, A. (2019). Fuzzy cognitive map based approach for determining the risk of ischemic stroke. IET systems biology, 13(6), 297-304.
[9] Bakhtavar, E., Valipour, M., Yousefi, S., Sadiq, R., & Hewage, K. (2021). Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex & Intelligent Systems, 7(2), 621-637.
[10] Shukla, D. K., & Sharma, M. Fuzzy Cognitive Maps based Mathematical Model for Prediction of “Parkinson Disease”.
[11] Bautista, J. A. R., Chaparro-Cárdenas, S. L., Hernández-Zavala, A., Gallegos-Torres, R. M., Zequera, M., Tovar-Barrera, Y., ... & Huerta-Ruelas, J. A. (2022). Fuzzy cognitive map to classify plantar foot alterations. IEEE Latin America Transactions, 20(7), 1092-2000.
[12] Obot, O., John, A., Udo, I., Attai, K., Johnson, E., Udoh, S., ... & Uzoka, F. M. (2023). Modelling differential diagnosis of febrile diseases with fuzzy cognitive map. Tropical Medicine and Infectious Disease, 8(7), 352.
[13] Akilashri, P., & Nithya, G. (2024). An Integrated Framework for Early Differential Diagnosis of COVID-19 using Improved Fuzzy Cognitive Map Approaches. Indian Journal of Science and Technology, 17(23), 2390-2400.
[14] Ruksakulpiwat, S., Phianhasin, L., Benjasirisan, C., & Schiltz, N. K. (2023). Using neural networks algorithm in ischemic stroke diagnosis: a systematic review, Journal of Multidisciplinary Healthcare, 16, 2593-2602.
[15] Hoyos, W., Aguilar, J., & Toro, M. (2023). PRV-FCM: An extension of fuzzy cognitive maps for prescriptive modeling. Expert Systems with Applications, 231, 120729.
[16] M. Khodadadi, L. Khodadadi, “A fuzzy expert system for predicting the risk of ischemic stroke based on modifiable risk factors,” Int. J. Smart Electr. Eng., in press.
[17] Khang, T. D., Vuong, N. D., Tran, M. K., & Fowler, M. (2020). Fuzzy C-means clustering algorithm with multiple fuzzification coefficients. Algorithms, 13(7), 158.
[18] Salman, Z., & Alomary, A. (2024). Performance of the K-means and fuzzy C-means algorithms in big data analytics. International Journal of Information Technology, 16(1), 465-470.
[19] Khanday, A. M. U. D., Rabani, S. T., Khan, Q. R., Rouf, N., & Mohi Ud Din, M. (2020). Machine learning based approaches for detecting COVID-19 using clinical text data. International Journal of Information Technology, 12(3), 731-739.