The research conducted in the medical domain addressed a topic of significant importance, steadily growing in relevance each year. The study focused on predicting the onset of strokes, a condition posing a grave risk to individuals' health and lives. Utilizing a highly imbalanced dataset posed a challenge in developing machine learning models capable of effectively predicting stroke occurrences. Among the models examined, the Random Forest model demonstrated the most promising performance, achieving precision, recall, and F1-score metrics of 90%. These findings hold potential utility for healthcare professionals involved in stroke diagnosis and treatment.
[1] Abedi V., Avula V., Chaudhary D., Shahjouei S., Khan A., Griessenauer C. J., Li J., Zand R. Prediction of Long-Term Stroke Recurrence Using Machine Learning Models. Journal of Clinical Medicine. 2021. Vol. 10, № 6. С. 1286. https://doi.org/10.3390/jcm10061286
[2] Melnykova N., Chereshchuk L. Application of machine learning methods for predicting the risk of stroke occurrence. Proceedings of the VI International Scientific and Practical Conference. Sofia, Bulgaria. 2023. pp. 210-216. International Science Group, 2023. ISBN 9798891451926.
[3] Ashrafuzzaman Md., Saha S., Nur K. Prediction of Stroke Disease Using Deep CNN Based Approach. Journal of Advances in Information Technology. 2022. Vol. 13, № 6. https://doi.org/10.12720/jait.13.6.604-613
[4] Sun X. Predictive model analysis of stroke disease based on machine learning. SPIE, 2023. https://doi.org/10.1117/12.2669554
[5] Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 2023.
[6] Biswas N., Uddin K. M. M., Rikta S. T., Dey S. K. A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach. Healthcare Analytics. 2022. Vol. 2. С. 100116. https://doi.org/10.1016/j.health.2022.100116
[7] Mostafa S. A., Elzanfaly D. S., Yakoub A. E. A Machine Learning Ensemble Classifier for Prediction of Brain Strokes. International Journal of Advanced Computer Science and Applications (IJACSA). 2022. Vol. 13,№ 12. https://doi.org/10.14569/IJACSA.2022.0131232
[8] Sailasya G., Kumari G. L. A. Analyzing the Performance of Stroke Prediction using ML Classification Algorithms. International Journal of Advanced Computer Science and Applications (IJACSA). 2021. Vol. 12, № 6. https://doi.org/10.14569/IJACSA.2021.0120662
[9] Khan M. K. Computer Science and Engineering.
[10] Uchida K., Kouno J., Yoshimura S., Kinjo N., Sakakibara F., Araki H., Morimoto T. Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML). Translational Stroke Research. 2022. Vol. 13, № 3. С. 370–381. https://doi.org/10.1007/s12975-021-00937-x
[11] Mezher M. A. Genetic Folding (GF) Algorithm with Minimal Kernel Operators to Predict Stroke Patients. Applied Artificial Intelligence. 2022. Vol. 36, № 1. С. 2151179. https://doi.org/10.1080/08839514.2022.2151179
[12] Tegistu B. S. Brain stroke prediction model using deep neural network (dnn). 2021.
[13] Pitchai R., Dappuri B., Pramila P. V., Vidhyalakshmi M., Shanthi S., Alonazi W. B., Almutairi K. M. A., Sundaram R. S., Beyene I. An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach. Computational Intelligence and Neuroscience. 2022. P. e5489084. https://doi.org/10.1155/2022/5489084
[14] Rohit A. P. V., Chowdary M. U., Ashish G. B. S., Anitha V., Sana S. Ml approach for brain stroke prediction using ist database. 2022. Vol. 7, № 10. https://doi.org/10.33564/IJEAST.2023.v07i10.008
[15] Telu V., Padimi V., Ningombam D. D. Optimizing Predictions of Brain Stroke Using Machine Learning. Journal of Neutrosophic and Fuzzy Systems. 2022. Vol. 2. С. 31–43. https://doi.org/10.54216/JNFS.020203
[16] DataHack : Biggest Data hackathon platform for Data Scientists.