# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 7 Result  
The brain stroke expectation framework separates individuals into okay, medium gamble, and high gamble classes in light of clinical and segment factors, for example, age, orientation, hypertension, coronary illness, smoking status, BMI, glucose levels, work type, and habitation. These boundaries are examined utilizing a blend of LSTM, CNN, and RNN profound learning models to distinguish complex connections and examples in the information. The technique permits medical care specialists to make preventive moves by recognizing high-risk patients right off the bat. The technique makes patients with high gamble aware of the requirement for early mediation, for example, way of life change projects or clinical treatments, to bring down the gamble of a stroke occurrence. Those in the okay gathering could profit from progressing, observing, and helping to keep up with their well-being through way of life changes like regular workouts and adjusted eating. People with medium gamble might require month-to-month subsequent meet-ups to guarantee that any new changes in their wellbeing pointers are seen and taken care of right away. Utilizing this delineated methodology, the framework works on individualized medical care, not just expanding the effectiveness of stroke preventive endeavors but also prompting better long-term wellbeing results. It adds to bringing down medical care costs by empowering centered activities that forestall strokes before they happen. Moreover, the framework's adaptability and association with EHR ensure that it stays current with every patient's developing wellbeing profile, so helping the precision and reliability of future conjectures. Table 4 and Figure 7 demonstrate that the models' performance was comparatively poorer before data augmentation. With an accuracy of 0.89 for the RNN model and 0.84 for the LSTM model, both models demonstrated their capacity to identify long-term dependencies. While getting a significant recall of 0.91, the CNN model had the lowest accuracy of 0.70. The hybrid model, which included many architectures, demonstrated its efficacy in stroke prediction by achieving the best accuracy of 0.92. These findings, however, imply that more advancements in data augmentation methods might be employed to increase model performance. Table 5 demonstrate how successful the suggested hybrid technique is when comparing the post-augmentation accuracy of various models. The accuracy scores of the RNN, LSTM, and CNN models were 0.94, 0.95, and 0.70, respectively. The precision and recall of the LSTM model were marginally higher. With an accuracy of 0.96, the hybrid model which combines many architectures performed better than any of the separate models. Figure 8 shows the accuracy trends over epochs show that the hybrid model continuously output forms standalone models in terms of accuracy and stability, hence enhancing its resilience in stroke prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RNN Model & 0.89 & 0.90 & 0.91 & 0.90 \\ \hline LSTM Model & 0.84 & 0.89 & 0.90 & 0.90 \\ \hline CNN Model & 0.70 & 0.74 & 0.91 & 0.83 \\ \hline Proposed Hybrid Model & 0.92 & 0.90 & 0.91 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics of deep learning models and hybrid approach. Figure 7: Accuracy comparison RNN, LSTM, CNN, and combined models. (**a**) Accuracy of RNN model, (**b**) accuracy of LSTM model, (**c**) accuracy of CNN model, (**d**) accuracy of hybrid model. As Table 6 illustrates, the model's stroke risk prediction findings reveal how different health parameters influence varying risk levels. In Case 1, a 35-year-old man without a history of heart disease, hypertension, or smoking is predicted by the model to have a low risk of stroke. The model identifies the risk as medium in Case 2, when the person is older (56 years), has hypertension, and has slightly raised blood sugar levels.