# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 2 Literature Review
### Machine Learning and Deep Learning in Stroke Detection  
Recent advancements in machine learning and deep learning demonstrate considerable potential in stroke diagnosis, utilizing diverse techniques to improve the prediction accuracy and efficiency [37]. A hybrid model integrating a stacked Convolutional Neural Network and Group Method of Data Handling (GMDH) with Long Short-Term Memory (LSTM) networks has been created for stroke prediction, attaining an accuracy of up to 99% through the analysis of electromyography (EMG) signals [38]. Enhanced deep learning models, including VGG16, ResNet50, and DenseNet121, have been optimized for brain stroke detection using MRI imagery, with ResNet50 achieving the highest accuracy of 95.67% [39]. The Optimized Ensemble Deep Learning (OEDL) method represents an innovative approach that combines clinical and radiomics features for the prognosis of acute ischemic stroke. It achieves a Macro-AUC of 97.89% and exhibits an enhanced classification performance relative to traditional methods [40]. Machine learning techniques such as Random Forest, XGBoost, and AdaBoost have been effectively utilized for early stroke prediction, with Random Forest attaining a classification accuracy of 99% [41]. These models employ data from medical reports and physical conditions to forecast stroke occurrences, demonstrating the capabilities of machine learning algorithms in real-time medical applications [42]. These studies demonstrate the effectiveness of integrating deep learning and machine learning techniques to enhance stroke diagnosis and prognosis, providing essential tools for early intervention and tailored treatment approaches [43].  
Artificial intelligence models provide notable benefits in cardiovascular diagnostics, particularly in terms of improved diagnostic accuracy, enhanced predictive capabilities, and increased scalability [44]. Machine learning algorithms are being applied to various cardiovascular conditions, including heart failure, valvular heart disease, and coronary artery disease. These applications improve diagnostic and prognostic capabilities, thereby enhancing patient care [45]. The application of AI to electrocardiograms (ECGs) has transformed the identification of hidden and imminent cardiac diseases, facilitating early intervention and potentially decreasing negative clinical outcomes [46]. Deep neural networks can detect intricate, non-linear alterations in ECGs, aiding in the diagnosis of conditions such as ventricular dysfunction and hypertrophic cardiomyopathy prior to the onset of symptoms [47]. Additionally, AI models can automate the interpretation of diagnostic images, thereby decreasing reliance on clinician expertise and accelerating the diagnostic process. Models such as Support Vector Machines have shown high predictive accuracy, reaching up to 97% in specific datasets, highlighting their efficacy in early disease detection. These models encounter limitations, particularly the necessity for comprehensive external validation via multicenter trials to establish reliability and generalizability. Moreover, AI models may exhibit biases if not adequately managed, potentially impacting their diagnostic accuracy and fairness. Despite these challenges, the integration of AI into healthcare systems has the potential to transform cardiovascular diagnostics, providing scalable solutions that can be incorporated into existing clinical practices and patient self-care technologies.