# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 6 Proposed Methodology
### Recurrent Neural Networks (RNNs)  
RNNs are powerful in learning consecutive connections inside a dataset. These organizations cycle input highlights by consolidating criticism circles, which permit the maintenance of data from past strides in a succession and catch fleeting conditions between data of interest [56]. For stroke expectation, RNNs are particularly helpful while breaking down time-reliant or consecutive information, for example, variances in glucose levels, BMI patterns, or other powerful wellbeing markers [57]. Despite the fact that RNNs succeed at demonstrating such examples, they might battle with long-term conditions and may require variations like LSTMs to further develop execution and stability [58].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Dataset** & **Algorithm Used** & **Technique** & **Advantages** & **Limitations** \\ \hline
[37, 38] & 5110 instances, 12,249:4861 split & LR, SGD, DT, AdaBoost, Gaussian, QDA, MLP, K-neighbors, GBC, XGB & Working with different ML models, imbalanced data & Exploring data on different ML models & Imbalanced data \\ \hline
[39, 40] & 5110 instances, 12,249:4861 split & LR, DT, RF, Voting classifier & SMOTE for balanced data & Better performance with upscaled data & Upscaled data may not be accurate for real-time \\ \hline
[41, 42] & 250:67,897 instances & DT, NB, neural network & Demographic data & Importance of demographic data on stroke prediction & No risk factors considered \\ \hline
[43, 44] & 29,072 instances, 12,548:28,524 split & DT, RF, neural networks & Feature correlation analysis, stepwise, PCA & Get optimum set of features & Limited features in dataset \\ \hline
[45, 46] & 2439 instances, 1556:1075 split & RLR, SVM, RF & ROS, RUS, SMOTE & Improve predictive performance with imbalanced data & Small dataset \\ \hline
[47, 48] & 43,400 instances, 12,548:28,524 split & Two-class decision jungle, two-class boosted decision tree & Chi-squared based feature selection & Extracts most important features from original dataset & Applicable only for categorical data \\ \hline
[1, 49] & 3522 instances, 3820:43,561 split & Deep neural network, RF, LR & Deep neural network & Multiple layers can represent complex outcomes in stroke patients & Small dataset \\ \hline
[50, 51] & 29,072 instances, 12,548:28,524 split & DT, RF, neural networks & PCA & Identifying the impact of risk factors on stroke prediction & Random downsampling technique \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of datasets and algorithms applied in stroke prediction studies.  
By examining sequential health data to identify temporal relationships in patient records, such as changes in blood pressure or heart rate over time, RNNs are used to predict brain strokes as illustrated in Figure 3[59]. By using its recurrent layers, the RNN examines this time-series input, taking into account both recent data and historical context at each stage as shown in Figure 3. This allows the model to comprehend how prior health patterns influence the risk of stroke [51; 60]. RNNs are appropriate over applications involving context-aware evaluation of sequential medical data because they can accurately forecast the chance of stroke through acquiring these temporal patterns [52; 61].