An-AI-Driven Approach for Early Detection and Classification of Stroke Variants

Abstract - Cerebrovascular accidents represent the second most prevalent cause of mortality worldwide and constitute a primary factor in chronic disability. These events arise from abrupt cessation of neuronal functionality due to oxygen deficiency, stemming from either obstructed cerebral circulation or arterial hemorrhage. The World Health Organization indicates that cerebrovascular mortality rates are projected to escalate in subsequent years. This research presents a computational intelligence framework for forecasting cerebrovascular events and categorizing their variants, specifically Thrombotic Stroke, Bleeding Stroke, and Mini-Stroke episodes. The framework employs algorithmic learning models developed using clinical patient information to predict probability and classification when fresh patient parameters are provided. Essential components encompass data refinement, threat assessment, and categorization utilizing methodologies including Bayesian Classification and Nearest Neighbor algorithms. Through delivering an autonomous and dependable clinical decision-assistance platform, the framework improves premature identification, minimizes manual errors, and assists medical practitioners in providing prompt prophylactic intervention. Additionally, the system can be expanded to incorporate extensive healthcare databases, facilitating enhanced adaptability and precision. It also enables visual performance analysis across different algorithms, ensuring superior result interpretation. Key Words: Algorithmic Learning, Bayesian Classification, Nearest Neighbor Algorithm (NNA), Medical Decision Assistance, Premature Detection, Threat Categorization, Thrombotic Stroke, Bleeding Stroke, Mini-Stroke Episode, Data Refinement, Algorithmic Performance Analysis, Medical Informatics, Discretization Technique, Patient Threat Prediction.