# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 11 Conclusions  
The brain stroke prediction framework, which employs hybrid DL methods (LSTM, CNN, and RNN), offers a promising approach to the early identification and prediction of stroke risk. Using these complicated neural network designs, the framework can assess numerical data from clinical and demographic characteristics and categorize stroke risk as low, medium, or high. This hybrid technique combines each model's capabilities, with LSTM excelling in time-series prediction, CNN effectively recognizing spatial patterns, and RNN competent at capturing sequential correlations in data. The framework's ability to accurately assess and identify risk levels demonstrates its potential to increase diagnostic precision and allow for prompt actions. Deep learning models are more resilient in handling complicated correlations between input variables, giving them an edge over classic stroke prediction algorithms. The combination (hybrid) model has an accuracy of 92%, with 90% precision, 91% recall, and a 90% F1-score. This high degree of performance demonstrates the hybrid approach's usefulness in making accurate stroke risk forecasts. Furthermore, the framework's scalability and integration features enable it to be used in a variety of healthcare contexts, including individual patient evaluations and population-level health monitoring. With the increasing availability of real-time monitoring via wearable devices and connection with EHR, this technique has the potential to improve stroke prediction and prevention. Its emphasis on individualized healthcare means that therapies are adapted to each patient's unique risk profile, leading to a more proactive approach to stroke therapy. As AI and deep learning technologies advance, the framework's capabilities will expand, paving the path for complete, data-driven solutions that improve patient outcomes, lower healthcare costs, and help in the overall battle against stroke. However, there are several obstacles and restrictions to consider. The model's accuracy and dependability are determined by the quality and amount of accessible data, and data biases may have an impact on the model's predictions. Furthermore, the intricate structure of deep learning models necessitates large computational resources, making real-time applications hard in resource-constrained environments. Furthermore, the models' interpretability remains a worry, as healthcare practitioners may struggle to grasp the logic behind the model's predictions, therefore reducing confidence and acceptance in clinical settings. Future research might focus on enhancing model explainability and combining various datasets to increase generalizability across populations. The use of other data sources, such as genetic information and lifestyle variables, may improve stroke risk prediction. Furthermore, investigating hybrid models that integrate deep learning with other machine learning approaches may enhance performance and resilience. The continuing improvement of wearable devices and IoT (Internet of Things) technology opens up the possibility of real-time stroke monitoring and prevention, enhancing the framework's potential to transform stroke management in the medical industry. Conceptualization, G.T., R.R. and C.P. ; methodology, G.T. and R.R. ; validation, G.T., R.R., C.P. and P.V. ; writing--original draft preparation, G.T., R.R. and C.P. ; writing--review and editing, G.T., R.R. and P.V. ; visualization, G.T. and R.R. ; supervision, P.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original data presented in the study are openly available online on [[https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset)]([https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset)) (accessed on 1 November 2024).