# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 3 Related Work  
Recent research has greatly improved stroke risk prediction with machine learning (ML) and deep learning (DL) approaches as shown in Table 1. [PERSON] et al. [1] used deep neural network techniques to increase prediction accuracy and identify key risk variables. Their approach included feature selection, data preprocessing, and model training with deep neural networks and machine learning. The study found that the random forest classifier attained 99% accuracy, outperforming the 4-layer artificial neural network (ANN) (92.39%); nonetheless, this high accuracy signals probable overfitting, restricting real-world applications. [PERSON] et al. [2] used an adaptive new fuzzy inference system (ANFIS) for stroke prediction based on Magnetic Resonance Imaging (MRI) data to handle uncertainty and increase accuracy. Their strategy includes preparing MRI data and comparing it to other ways. The model significantly improved performance indicators such as accuracy, sensitivity, specificity, F1-score, and ROC analysis, but it was heavily dependent on parameter selection, which hampered dependability. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Title of Paper** & **Objective** & **Methodology** & **Result/Limitations** \\ \hline \multirow{7}{*}{1.} & \"Prediction of Brain & \(\bullet\) & To improve forecast accuracy and pinpoint important risk indicators, apply deep neural network & The strategy consists of feature selection, data preprocessing, model training utilizing deep neural networks and machine learning ANN, and assessment to determine the best accurate way for predicting brain strokes. & The random forest classifier reached 99\% accuracy, surpassing the 4-layer ANN by 92.39\%. However, its high accuracy indicates overfitting, which may limit real-world usefulness. & The random forest classifier reached 99\% accuracy, surpassing the 4-layer ANN by 92.39\%. However, its high accuracy indicates overfitting, which may limit real-world usefulness. & The paper introduces an ANFIS-based stroke prediction model, achieving performance metrics like accuracy, precision, sensitivity, specificity, F1-score, and ROC analysis, compared to other classifiers. & The suggested DNN with weighted BCE loss enhanced stroke identification for the minority class, increasing recall, precision, and F1-score while decreasing accuracy from 95.36\% to 75.36\%, potentially affecting performance in accuracy-critical instances & The suggested DNN with weighted BCE loss enhanced stroke identification for the minority class, increasing recall, precision, and F1-score while decreasing accuracy from 95.36\% to 75.36\%, potentially affecting performance in accuracy-critical instances & The CNN-bidirectional LSTM model predicted strokes with 94\% accuracy, although it may face data quality and noise issues in reality. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature survey on brain stroke detection and prediction system. [PERSON] et al. [3] developed a deep neural network that uses weighted binary cross-entropy to improve stroke detection accuracy. The process included training a deep neural network (DNN) on brain imaging data and prioritizing stroke detection with a weighted loss function. The model considerably increased recall, precision, and F1-score while decreasing overall accuracy from 95.36% to 75.36%, which may influence performance when accuracy is critical. [PERSON] et al. [4] created a real-time stroke prediction system with electroencephalogram (EEG) data and DL models, including CNN-LSTM and bidirectional LSTM. Their approach included real-time biosignal collection, feature extraction, and prediction with deep learning models. The CNN-bidirectional LSTM model obtained 94% accuracy; however, changes in data quality and noise between patients may impact real-world deployment. [PERSON] et al. [5] investigated CNN-based stroke diagnosis using medical imaging data.