70 Identification of Ischemic and Hemorrhagic Strokes with the Aid of Machine Learning and Deep Learning

Abstract

Stroke is a leading cause of mortality and disability worldwide, necessitating rapid and accurate diagnosis for effective intervention. This study focuses on leveraging machine learning (ML) and deep learning (DL) techniques to distinguish between ischemic and hemorrhagic strokes using clinical and imaging data. The proposed methodologies aim to enhance diagnostic precision, reduce time to treatment, and optimize healthcare outcomes. A dataset comprising neuroimaging scans (e.g., CT and MRI) and clinical parameters (e.g., age, comorbidities, symptom onset) was analyzed. Various ML algorithms, including random forests and support vector machines, were utilized for feature selection and preliminary classification. Additionally, convolutional neural networks (CNNs) were employed to process imaging data for stroke type identification. Hybrid models integrating clinical and imaging features were developed to achieve a holistic diagnostic framework. Performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were used to evaluate the models. Preliminary results indicate that the deep learning models achieved higher diagnostic accuracy up to 95% compared to traditional machine learning approaches. The hybrid models demonstrated superior performance, showcasing the importance of combining imaging and clinical data for robust stroke diagnosis. This research highlights the potential of ML and DL in revolutionizing stroke diagnosis, addressing the challenges of misclassification and delayed identification. The findings advocate for integrating AI-driven diagnostic tools into clinical workflows to support medical decision-making and improve patient outcomes. Future work involves validating the models on larger, diverse datasets and exploring their adaptability in real-world clinical settings.