Early Detection of Hemorrhagic Stroke Using Machine Learning Techniques

Abstract The purpose of this work is to construct a predictive model for hemorrhagic stroke using machine learning techniques. The emphasis is on designing a robust prediction system capable of reliably identifying people at risk. Materials and Methods: The present study comprised two groups. Group 1 refers to a Support Vector Machine (SVM) approach with high gradient boosting, whereas Group 2 belongs to the Convolutional Neural Network technique that enhances accuracy and produces faster results. Results: The review examines several machine learning methods, with a special emphasis on Convolutional Neural Networks (CNN), which achieve outstanding accuracy levels of 97%, while the existing approach of support vector machines reaches 96% in prediction. Conclusion: Within the limits of this study, the Convolutional neural network achieves exceptional accuracy in predicting hemorrhagic stroke.