Data Driven Deep Learning Model for Effective Stroke Prediction

The substantial impact of stroke on society has driven continuous efforts to enhance its diagnosis and management. The growing integration of technology with medical diagnostics empowers healthcare providers to optimize patient care by systematically mining and archiving medical records. This study proposes a Data-Driven Deep Learning (DDDL) model for effective stroke prediction, employing an Attention Residual Network to boost predictive accuracy and support timely clinical interventions. The input data is subjected to a preprocessing stage, where irrelevant and redundant information is cleaned and removed. The Exploratory Data Analysis (EDA) visualization phase incorporates data training and testing, offering valuable insights for further analysis. Classification is executed using the Attention Residual Network, ensuring accurate and dependable predictions. The predicted output aids in the early detection of stroke, enabling prompt clinical decision-making. Python software is utilized for simulation with a accuracy of 90%, validating the model's performance and demonstrating its potential to enhance stroke prediction and improve patient outcomes.