# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 5 Dataset Used  
Healthcare predictive modeling is a strategy that uses past data to anticipate probable future health issues [24]. The work makes use of a publicly available dataset on brain stroke detection, obtained from GitHub [25]. Table 2 provides the details of the dataset and its attributes used in the current study. Several biomarkers and risk variables, such as gender, residence type, BMI, glucose level, hypertension, heart disease, smoking status, and work type as mention in Table 2. are used to develop models for individual risk stratification in stroke [26]. Table 3 provides an overview on various datasets, predictive models and their performance for stroke prediction. Each of these factors has a significant impact on the chance of a stroke:  
* Gender (Risk Factor): It may be established that gender is an important predictor of stroke. Several studies have revealed that men are more likely than women to have a stroke; however, this changes with age [26]. The integration of gender into this model enables personalized predictions based on gender-related risk indicators [27]. * Residence Type (Risk Factor): Whether an individual lives in an urban or rural area influences lifestyle aspects such as food, exercise regimen, healthcare access, and stress levels, all of which increase stroke risk. Variations in eating choices and healthcare access between urban and rural populations can influence stroke risk [28]. * Body Mass Index (BMI) (Biomarker): BMI is an important biomarker used worldwide to calculate body fat percentage. It is linked to a variety of health issues, including stroke, hypertension, diabetes, and cardiovascular disease. Individuals with a high BMI have a considerably higher risk of stroke [29; 30]. * Glucose Level (Biomarker): High glucose levels are a biomarker for diabetes, which is a major risk factor for stroke. Chronic high blood sugar levels can damage blood vessels, leading to ischemic and hemorrhagic strokes [29; 31]. * Hypertension (Biomarker): Hypertension (high blood pressure) is an important biomarker for stroke because it weakens blood vessels and increases the risk of clotting. Controlling blood pressure is critical for stroke prevention and risk assessment in prediction models [29; 32]. * Heart Disease (Biomarker): Atrial fibrillation and coronary artery disease are biomarkers that dramatically increase the risk of stroke. These diseases can cause clot development, which, when carried to the brain, can result in a stroke. The addition of cardiac disease in prediction models improves stroke prognosis accuracy [29; 33]. * Smoking Status (Risk Factor and Biomarker): Smoking is both a modifiable biomarker and a risk factor for stroke. Cigarette chemicals, notably nicotine, lead to hypertension, low oxygen levels, and vascular damage, raising stroke risk [34]. \begin{table}
\begin{tabular}{l l} \hline
**Attribute Name** & **Description** \\ \hline ID of individual & Unique identification number of 5110 patient \\ \hline Gender & Male = 0, female = 1 \\ \hline Age (in year) & Age of the patient (1–82) \\ \hline Hypertension & Indicating whether the patient has hypertension (1) problem or not (0) \\ \hline Heart disease & Demonstrating whether the unique id patient has heart diseases \\  & problem (1) or not (0) \\ \hline Ever married & Represent the marital status by yes (1) or no (0). \\  & It indicates five category of work status of the patient. \\ \hline Work type & Government job, private, self employed, children, never worked \\ \hline Residence type & It Denotes the residential area type, whether Rural = “0” or \\  & Urban = “1” \\ \hline Average glucose & It gives the average glucose level which is represented in \\ level & numeric form. \\ \hline BMI & Body mass index is represented in numeric form. \\ \hline Smoking status & It indicates three categories of smokers. \\  & Smokes, never smoked, Formerly Smokes \\ \hline Stroke & It indicates the target, whether have stroke (1) or non-stroke (0). \\ \hline \end{tabular}
\end{table}