NeuroNet: A Deep Learning Framework for Stroke Analysis

The revolutionary potential of a deep learning framework for stroke analysis and prediction in the healthcare industry is examined in this paper. The suggested framework uses large datasets, such as patient records, medical images, and pertinent health information, to create histicated neural networks in order to address the critical need for early detection and intervention in stroke cases. Important areas like stroke type classification, natural language processing, image analysis, treatment optimization, outcome prediction, telemedicine, and explainable artificial intelligence are highlighted in the thorough review of related work. The steps involved in data collection, preprocessing, model selection, and result interpretation are all carefully laid out in the methodology. A thorough evaluation of machine learning models, such as Random Forest, SVM, XGBoost, and Logistic Regression, is carried out; Random Forest continuously performs well. The results highlight how difficult it is to predict strokes and how crucial it is to choose models carefully. With the ultimate goal of improving stroke diagnosis, treatment, and prevention for improved patient care through ongoing research and technological advancements, ethical considerations and adherence to healthcare regulations in AI applications are emphasized.