J. Fan et al., “Global Burden, Risk Factor Analysis, and Prediction Study of Ischemic Stroke, 1990-2030,” Neurology, vol. 101, no. 2, 2023, doi: https://doi.org/10.1212/WNL.0000000000207387.

H. S. Nam and B. M. Kim, “Advance of Thrombolysis and Thrombectomy in Acute Ischemic Stroke,” Journal of Clinical Medicine, vol. 12, no. 2. 2023, doi: https://doi.org/10.3390/jcm12020720.

Cynthia and D. Aulia, “Ischemic Stroke with Anticoagulant Protein C Deficiency,” Int. J. Sci. Soc., vol. 5, no. 1, 2023, doi: https://doi.org/10.54783/ijsoc.v5i1.657.

J. Kong, R. Chu, and Y. Wang, “Neuroprotective Treatments for Ischemic Stroke: Opportunities for Nanotechnology,” Advanced Functional Materials, vol. 32, no. 52. 2022, doi: https://doi.org/10.1002/adfm.202209405.

B. L. Clary, C. B. Beswick, O. Golestanian, K. M. Barnes, B. Pacheco Pereira, and C. M. Brown, “Abstract 19: Loss Of Endothelial Tissue-nonspecific Alkaline Phosphatase Modifies Sensorimotor Deficits In Chronic Ischemic Stroke,” Stroke, vol. 54, no. Suppl_1, 2023, doi: https://doi.org/10.1161/str.54.suppl_1.19.

A. Barkhudaryan, W. Doehner, and N. Scherbakov, “Ischemic stroke and heart failure: Facts and numbers. an update,” Journal of Clinical Medicine, vol. 10, no. 5. 2021, doi: https://doi.org/10.3390/jcm10051146.

D. M. Williams and A. C. G. Felix, “Prevention, Diagnosis, and Management of Stroke,” in Reichel’s Care of the Elderly, 2022.

Google Scholar 

C. Anton-Munarriz, R. Pastor-Vargas, J. M. Haut, A. Robles-Gomez, M. E. Paoletti, and J. Alberto Benitez-Andrades, “Detection of cerebral ischaemia using transfer learning techniques,” in Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2023, vol. 2023-June, doi: https://doi.org/10.1109/CBMS58004.2023.00284.

X. Guo and J. Dye, “Modern Prehospital Screening Technology for Emergent Neurovascular Disorders,” Advanced Biology, vol. 7, no. 10. 2023, doi: https://doi.org/10.1002/adbi.202300174.

F. Alshehri, “Imaging Based Detection of Acute Ischemic Stroke Via Multidetector Computed Tomography,” J. Umm Al-Qura Univ. Med. Sci., vol. 9, no. 1, 2023, doi: https://doi.org/10.54940/ms94397891.

R. Kurokawa, M. Kurokawa, A. Baba, J. Kim, A. Srinivasan, and T. Moritani, “Dynamic susceptibility contrast perfusion-weighted and diffusion-weighted magnetic resonance imaging findings in pilocytic astrocytoma and H3.3 and H3.1 variant diffuse midline glioma, H3K27-altered,” PLoS One, vol. 18, no. 7 2023, doi: https://doi.org/10.1371/journal.pone.0288412.

J. B. Schulman et al., “DSC MRI in the human brain using deoxyhemoglobin and gadolinium—Simulations and validations at 3T,” Front. Neuroimaging, vol. 2, 2023, doi: https://doi.org/10.3389/fnimg.2023.1048652.

O. Tritanon, W. Boonsang, T. Chansakul, P. Jindahra, and T. Panyaping, “Diagnostic Value of Dynamic Contrast-Enhanced (DCE), Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labeling (ASL) Perfusion MRI for Differentiation of High-Grade and Low-Grade Gliomas,” J. Med. Assoc. Thail., vol. 106, no. 5, 2023, doi: https://doi.org/10.35755/jmedassocthai.2023.05.13845.

V. Chanabasanavar, “Susceptibility weighted imaging technique utility in diverse neurological entities,” MNJ (Malang Neurol. Journal), vol. 9, no. 2, 2023, doi: https://doi.org/10.21776/ub.mnj.2023.009.02.2.

C. I. Coleman, M. Concha, B. Koch, B. Lovelace, M. J. Christoph, and A. T. Cohen, “Derivation and validation of a composite scoring system (SAVED2) for prediction of unfavorable modified Rankin scale score following intracerebral hemorrhage,” Front. Neurol., vol. 14, 2023, doi: https://doi.org/10.3389/fneur.2023.1112723.

M. Y. Zhang, M. Mlynash, K. L. Sainani, G. W. Albers, and M. G. Lansberg, “Ordinal Prediction Model of 90-Day Modified Rankin Scale in Ischemic Stroke,” Front. Neurol., vol. 12, 2021, doi: https://doi.org/10.3389/fneur.2021.727171.

N. Niznick et al., “Patient Relevance of the Modified Rankin Scale in Subarachnoid Hemorrhage Research: An International Cross-sectional Survey,” Neurology, vol. 100, no. 15, 2023, doi: https://doi.org/10.1212/WNL.0000000000206879.

Y. Guo et al., “A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke,” J. Clin. Med., vol. 11, no. 18, 2022, doi: https://doi.org/10.3390/jcm11185364.

Y. Guo et al., “The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction,” Life, vol. 12, no. 11, 2022, doi: https://doi.org/10.3390/life12111847.

S. Campagnini, C. Arienti, M. Patrini, P. Liuzzi, A. Mannini, and M. C. Carrozza, “Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1. 2022, doi: https://doi.org/10.1186/s12984-022-01032-4.

L. Fast et al., “Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke,” Front. Neurol., vol. 14, 2023, doi: https://doi.org/10.3389/fneur.2023.1114360.

U. Singh, A. K. Jena, and M. T. Haque, “An Ensemble Learning Approach and Analysis for Stroke Prediction Dataset,” 2022, doi: https://doi.org/10.1109/ASSIC55218.2022.10088363.

Book  Google Scholar 

M. Alruily, S. A. El-Ghany, A. M. Mostafa, M. Ezz, and A. A. A. El-Aziz, “A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction,” Appl. Sci., vol. 13, no. 8, 2023, doi: https://doi.org/10.3390/app13085047.

H. M. Dragoș et al., “MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome—A Systematic Review,” Diagnostics, vol. 13, no. 5. 2023, doi: https://doi.org/10.3390/diagnostics13050857.

W. Ye et al., “OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features,” Front. Neurol., vol. 14, 2023, doi: https://doi.org/10.3389/fneur.2023.1158555.

T. Polidori et al., “Radiomics applications in cardiac imaging: a comprehensive review,” Radiol. Medica, vol. 128, no. 8, 2023, doi: https://doi.org/10.1007/s11547-023-01658-x.

X. Wen, X. Hu, Y. Xiao, and J. Chen, “Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic stroke,” Diagnostic Interv. Radiol., vol. 29, no. 2, 2023, doi: https://doi.org/10.4274/dir.2023.221764.

S. Lee et al., “Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation,” PLoS One, vol. 18, no. 5, 2023, doi: https://doi.org/10.1371/journal.pone.0286417.

H. Yu et al., “Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics,” Front. Psychiatry, vol. 13, 2023, doi: https://doi.org/10.3389/fpsyt.2022.1105496.

A. Gerbasi et al., “Prognostic Value of Combined Radiomic Features from Follow-Up DWI and T2-FLAIR in Acute Ischemic Stroke,” J. Cardiovasc. Dev. Dis., vol. 9, no. 12, 2022, doi: https://doi.org/10.3390/jcdd9120468.

J. Liu et al., “Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics,” Front. Neurosci., vol. 17, 2023, doi: https://doi.org/10.3389/fnins.2023.1110579.

R. Shree et al., “Application of Ensemble Methods in Medical Diagnosis,” 2023.

Book  Google Scholar 

S. M. Smith et al., “Advances in functional and structural MR image analysis and implementation as FSL,” NeuroImage, 2004, vol. 23, no. SUPPL. 1, doi: https://doi.org/10.1016/j.neuroimage.2004.07.051.

Y. Guo et al., “Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome,” Diagnostics, vol. 12, no. 7, 2022, doi: https://doi.org/10.3390/diagnostics12071664.

T. Cai, “Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method,” Appl. Comput. Math., vol. 7, no. 3, 2018, doi: https://doi.org/10.11648/j.acm.20180703.20.

D. Elreedy and A. F. Atiya, “A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance,” Inf. Sci. (Ny)., vol. 505, 2019, doi: https://doi.org/10.1016/j.ins.2019.07.070.

S. E. Roshan and S. Asadi, “Improvement of Bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization,” Eng. Appl. Artif. Intell., vol. 87, 2020, doi: https://doi.org/10.1016/j.engappai.2019.103319.

D. E. Nayab, R. U. Khan, and A. M. Qamar, “Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets,” Appl. Comput. Intell. Soft Comput., vol. 2023, 2023, doi: https://doi.org/10.1155/2023/5542049.

Y. Cassuto and Y. Kim, “Boosting for Straggling and Flipping Classifiers,” in IEEE International Symposium on Information Theory - Proceedings, 2021, vol. 2021-July, doi: https://doi.org/10.1109/ISIT45174.2021.9517745.

A. Onan, “Particle Swarm Optimization Based Stacking Method with an Application to Text Classification,” Acad. Platf. J. Eng. Sci., vol. 6, no. 2, 2018, doi: https://doi.org/10.21541/apjes.329940.

M. Lanes, E. N. Borges, and R. Galante, “The effects of classifiers diversity on the accuracy of stacking,” 2017, doi: https://doi.org/10.18293/SEKE2017-016.

Book  Google Scholar 

L. Liu, X. Wu, S. Li, Y. Li, S. Tan, and Y. Bai, “Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection,” BMC Med. Inform. Decis. Mak., vol. 22, no. 1, 2022, doi: https://doi.org/10.1186/s12911-022-01821-w.

W. Feng, J. Gou, Z. Fan, and X. Chen, “An ensemble machine learning approach for classification tasks using feature generation,” Conn. Sci., vol. 35, no. 1, 2023, doi: https://doi.org/10.1080/09540091.2023.2231168.

D. Muller, I. Soto-Rey, and F. Kramer, “An Analysis on Ensemble Learning Optimized Medical Image Classification with Deep Convolutional Neural Networks,” IEEE Access, vol. 10, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3182399.

L. P. Jin and J. Dong, “Ensemble Deep Learning for Biomedical Time Series Classification,” Comput. Intell. Neurosci., vol. 2016, 2016, doi: https://doi.org/10.1155/2016/6212684.

H. Zhu, L. Jiang, H. Zhang, L. Luo, Y. Chen, and Y. Chen, “An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging,” NeuroImage Clin., vol. 31, 2021, doi: https://doi.org/10.1016/j.nicl.2021.102744.

J. Lu et al., “Determining acute ischemic stroke onset time using machine learning and radiomics features of infarct lesions and whole brain,” Math. Biosci. Eng., vol. 21, no. 1, pp. 34–48, 2023, doi: https://doi.org/10.3934/mbe.2024002.

Article  Google Scholar