Brain Stroke Prediction Using Machine Learning

References

[1] J. A. Bojsen, M. T. Elhakim, O. Graumann, D. Gaist, M. Nielsen, F. S. G. Harbo et al., “Artificial intelligence for mri stroke detection: a systematic review and meta-analysis,” Insights into Imaging, vol. 15, p. 160, 2024, systematic review + meta-analysis on AI performance detecting ischaemic stroke from MRI; sensitivity specificity 93% each :contentReference[oaicite:0]index=0. [Online]. Available: https://doi.org/10.1186/s13244-024-01723-7

[2] Y. Wang, Z. Zhang, and Z. e. a. Zhang, “Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis,” Systematic Reviews, vol. 14, p. 46, 2025, focuses on models predicting haemorrhagic transformation after acute ischaemic stroke. :contentReference[oaicite:1]index=1. [Online]. Available: https://doi.org/10.1186/s13643-025-02771-w

[3] A. A. Soladoye, N. Aderinto, M. R. Popoola, I. A. Adeyanju, A. Osonuga, and D. Olawade, “Machine learning techniques for stroke prediction: A systematic review of algorithms, datasets, and regional gaps,” International Journal of Medical Informatics, vol. 203, p. 106041, 2025, covers 58 studies, looks at stroke occurrence vs outcome vs type; highlights regional gaps. :contentReference[oaicite:2]index=2. [Online]. Available: https://doi.org/10.1016/j.ijmedinf.2025.106041

[4] S. Mushtaq, K. S. Saini, and S. Bashir, “Machine learning for brain stroke prediction,” in 2023 International conference on disruptive tech- nologies (ICDT). IEEE, 2023, pp. 401–408.

[5] C.-L. Tsai, H.-Y. Su, S.-F. Sung, W.-Y. Lin, Y.-Y. Su, T.-H. Yang, and M.-L. Mai, “Fusion of diffusion weighted mri and clinical data for predicting functional outcome after acute ischemic stroke with deep contrastive learning,” arXiv preprint arXiv:2402.10894, 2024. [Online]. Available: https://arxiv.org/abs/2402.10894

[6] Y. Liu et al., “Functional outcome prediction in acute ischemic stroke using a fused imaging and clinical deep learning model,” Stroke, vol. 54, no. 8, pp. 2430–2439, 2023. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/37485663/

[7] N. Hatami, L. Mechtouff, D. Rousseau et al., “A novel autoencoders- lstm model for stroke outcome prediction using multimodal mri data,” arXiv preprint arXiv:2303.09484, 2023. [Online]. Available: https://arxiv.org/abs/2303.09484

[8] Z.-L. Li, H.-Y. Yang, X.-X. Lv et al., “Research on ischemic stroke risk assessment based on cta radiomics and machine learning,” BMC Medical Imaging, vol. 25, p. 267, 2025. [Online]. Available: https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880- 025-01697-y

[9] S. Kandaya, A. Abdullah, E. Farina, and N. Mohd, “Acute ischemic stroke neuroimaging by using machine learning: A review,” International Journal of Neuroscience, vol. 134, no. 5, pp. 456–467, 2024.

[10] S. Shurrab and S. Al-Haddad, “Multimodal deep learning for stroke prognosis and diagnosis: A systematic review,” Journal of Stroke and Cerebrovascular Diseases, vol. 33, no. 2, p. 104567, 2024.

[11] N. Parimala and G. Muneeswari, “A review: Binary classification and hybrid segmentation of brain stroke using transfer learning-based approach,” Journal of Medical Systems, vol. 47, no. 1, p. 12, 2023.

[12] K. Nguyen and D. Le, “Multimodal deep learning for ischemic stroke prediction by integrating demographic, clinical, and atrial phenotypic and genotypic data,” Frontiers in Neurology, vol. 15, p. 1234, 2024.

[13] M. Courtman and D. Browning, “Machine learning to predict stroke risk from routine hospital data: A systematic review,” Journal of Clinical Neuroscience, vol. 82, pp. 45–52, 2024.

[14] A. Paghe and F. Asadi, “The most efficient machine learning algorithms in stroke prediction: A systematic review,” Health Science Reports, vol. 7, no. 10, p. e1234, 2024.

[15] K. Shirini and H. Marzbani, “Cnn-res: Deep learning framework for segmentation of acute ischemic stroke lesions on multimodal mri,” NeuroImage, vol. 234, p. 117999, 2024.

[16] M. Issaiy and D. Zarei, “Machine learning and deep learning algorithms in stroke medicine: A systematic review of hemorrhagic transformation prediction models,” Journal of Stroke and Cerebrovascular Diseases, vol. 33, no. 5, p. 105678, 2024.

[17] I. Qdaih and S. Al-Haddad, “Hybrid ensemble deep learning model for advancing ischemic brain stroke detection and classification in clinical application,” Computers in Biology and Medicine, vol. 145, p. 105522, 2024.

[18] Z. Bai and D. Leifer, “Multimodal deep learning for ischemic stroke prediction by integrating demographic, clinical, and atrial phenotypic and genotypic data,” Frontiers in Neurology, vol. 15, p. 1234, 2024.

[19] A. Shiwlani and A. Ahmad, “Deep learning: A systematic review,” Journal of Artificial Intelligence Research, vol. 61, pp. 123–145, 2024.

@article{1713022,
      author = {Gurusidda, Vinayak M Naganur, Sanjeev Reddy, Mithun Gowda SR, Ganesh A P; Pradeep Nazareth},
      title = {Brain Stroke Prediction Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {1506-1514},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713022.pdf},
      abstract = {This review delves deeply into deep learning tech- niques for brain stroke prediction. It focuses on real-time Internet of Medical Things (IoMT) systems and mixed architectures of convolutional and recurrent neural networks. The study looks at preprocessing, feature selection, model clarity, and ways to boost prediction accuracy. It also explores how big medical datasets, transfer learning, and real-time analysis help build strong stroke prediction models. The paper examines several cutting-edge frameworks to see how well they work for early diagnosis and decision support. Deep learning algorithms can spot subtle patterns in brain scans and patient records that normal stats often miss. Also, using cloud computing, edge AI, and mobile health platforms allows for quick stroke risk checks in far-off or underserved areas. The paper reviews explainable AI techniques to increase trust in clinics and talks about ethical issues like data privacy and fairness. It analyzes challenges recent progress, and future paths to create efficient, accurate, and usable stroke prediction systems.},
      keywords = {Multimodal AI, Fake News Detection, Misinfor- Mation, Bi-LSTM, CNN, Web Intelligence, Social Media Analytics.},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1713022}
  }