# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 6 Proposed Methodology
### Hybrid Deep Learning Model  
Profound learning has shown to be an amazing asset in clinical conclusions by empowering the extraction of highlights from complex datasets, including patient narratives, wellbeing markers, and physiological signs, without the requirement for express programming [76; 77]. Nonetheless, independent profound learning models, even profound semantic organizations, frequently battle with summed-up issues and boisterous information, which are normal in clinical datasets. To defeat these difficulties, half-breed profound learning models have been created by consolidating different structures to improve prescient capabilities as shown in Figure 6[78; 79]. In mind stroke forecast and discovery, especially utilizing numeric datasets, crossover models can coordinate CNNs, recurrent neural networks (RNNs), and long transient memory organizations (LSTMs) to deal with patient boundaries, for example, orientation, glucose level, BMI, work type, hypertension, coronary illness, and smoking status [80; 81]. Figure 6 provides the workflow that has been adopted in current study for application of hybrid DL model for brain stroke prediction. * Data Collection: The method begins with gathering relevant information from various sources, including electronic health records (EHRs), clinical datasets, and shrewd wellbeing checking gadgets [82]. This information contains both static components, for example, BMI, glucose levels, hypertension status, and segment data, and dynamic elements, which record fleeting changes in wellbeing markers, for example, day-to-day glucose level swings or circulatory strain patterns over the long run [83]. Thorough and enhanced information gathering guarantees that the model gets a general image of patient gamble and wellbeing factors [84]. * Data Pre-processing: The technique begins with gathering relevant information from different sources, including EHRs, clinical datasets, and savvy well-being-observing gadgets [85]. This information contains both static components, for example, BMI, glucose levels, hypertension status, and segment data, and dynamic highlights, which record transient changes in wellbeing pointers, for example, everyday glucose level swings or pulse patterns over the long term [86]. Exhaustive and enhanced information gathering guarantees that the model gets a general image of patient gamble and wellbeing factors [87]. * Training Data: Following the preprocessing stage, the information is isolated into two datasets: preparing and testing. A preparation set has been used to prepare the cross-breed model, permitting it to distinguish examples and relationships in the information [83; 86]. The testing dataset, which was held back all through preparing, is subsequently utilized for evaluating the model's exhibition on already concealed information, guaranteeing that it sums up really and maintains a strategic distance from overfitting [88]. This division is basic to making a reliable and strong forecast framework. * Hybrid Model: The crossover model proposes the qualities of three particular brain network plans to deal with both static and dynamic elements effectively: RNN Component: This module dissects consecutive information, catching examples and fleeting connections in unique qualities like glucose levels and circulatory strain variances across time [89; 90]. LSTM Component: In light of the RNN, LSTMs succeed at grasping conditions that go the distance over the long run, permitting the model to understand connections that range over extensive spans, for example, what relentless hypertension might mean for stroke risk [90]. CNN Component: This module centers around static boundaries like BMI, glucose levels, and hypertension, removing itemized designs and deciding connections between them [91]. The CNN's ability to catch complex component communicationsfurther develops general forecast precision. These components work together to guarantee that the model can fully process and incorporate both static and temporal elements of the input.