# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## Abbreviations
Healthcare. Eng._**2022**, _2022_, 9276579. [CrossRef] [PubMed]
* (35) [PERSON]; [PERSON] [PERSON]; [PERSON] ChronoNet: A deep recurrent neural network for abnormal EEG identification. In Proceedings of the Artificial Intelligence in Medicine: 17 th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, 26-29 June 2019; Proceedings 17. Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 47-56. * (36) [PERSON]; [PERSON] Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: A predicting framework. _Sci. Rep._**2024**, _14_, 20053. * (37) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Performance analysis of machine learning approaches in stroke prediction. In Proceedings of the 2020 4 th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 5-7 November 2020; IEEE: New York, NY, USA; pp. 1464-1469. * (38) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON] Stroke disease detection and prediction using robust learning approaches. _J. Healthcare. Eng._**2021**, _2021_, 7633381. [CrossRef]
* (39) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Stroke risk prediction model based on demographic data. In Proceedings of the 2015 8 th Biomedical Engineering International Conference (BMEiCON), Pattaya, Thailand, 25-27 November 2015; IEEE: New York, NY, USA; pp. 1-3. * (40) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Artificial neural network methods for the solution of second order boundary value problems. _Comput. Mater. Contin._**2019**, _59_, 345-359. [CrossRef]
* (41) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Predicting stroke from electronic health records. In Proceedings of the 2019 41 st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23-27 July 2019; IEEE: New York, NY, USA; pp. 5704-5707. * (42) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A predictive analytics approach for stroke prediction using machine learning and neural networks. _Healthc. Anal._**2022**, \(2\), 100032. [CrossRef]
* (43) [PERSON]; [PERSON] Stroke prediction with machine learning methods among older Chinese. _Int. J. Environ. Res. Public Health_**2020**, _17_, 1828. [CrossRef]
* (44) [PERSON]; [PERSON]; [PERSON] A deep collocation method for the bending analysis of Kirchhoff plate. _arXiv_**2021**, arXiv:2102.02617. [CrossRef]
* (45) [PERSON]; [PERSON] Advances in Machine Learning for Stroke Prediction: A Comprehensive Review. In Proceedings of the 2024 8 th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 3-5 October 2024; IEEE: New York, NY, USA; pp. 1798-1802. * (46) [PERSON]; [PERSON] Prediction of stroke using deep learning model. In Proceedings of the Neural Information Processing: 24 th International Conference, ICONIP 2017, Guangzhou, China, 14-18 November 2017; Proceedings, Part V 24. Springer International Publishing: Berlin/Heidelberg, Germany; pp. 774-781. * (47) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region. _Comput. Biol. Med._**2025**, _186_, 109711. [CrossRef] [PubMed]
* (48) [PERSON]; [PERSON] A hybrid system to predict brain stroke using a combined feature selection and classifier. _Intell. Med._**2024**, \(4\), 75-82. [CrossRef]
* (49) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Infarction of 'non-core-non-penumbral'tissue after stroke: Multivariate modelling of clinical impact. _Brain_**2011**, _134_, 1765-1776. [CrossRef]
* (50) [PERSON]; [PERSON]; [PERSON] Machine learning based approach using XGboost for heart stroke prediction.