Effectiveness of Stroke Attack Risk Assessmentusing Application Face Drop Recognition

Abstract

Introduction:The treating of a stroke has a limited period; if it is not discovered immediately, it can lead to complications and even death. Objective: This study used innovations in technology to detect the danger of stroke early, allowing it to be treated rapidly and boost the probability of recovery. Based on this goal, a Stroke Attack Risk Assessment Using the Application Face Drop Recognition was created.The risk assessment related to the occurrence of stroke is evaluated by theface image utilizing analytical and face detection technology. Method: This is a descriptive-analytic study. The research population of 60 farmers was selected. Respondents in this study came from agricultural communities in rural area distant from hospital referral facilities, were at risk of stroke, or had a history of stroke. To assess the accuracy of face drop detection apps utilizing criteria such as accuracy, precision, recall, and F1-score. Result: The testing was limited to 95% accuracy. This suggests that the capacity to predict stroke risk for persons who have had a stroke is very good. The application's specificity value was 100%, which means that the measuring capacity of the application produces a nonstroke result of 83%.The results of this technology can suggest to immediately make an emergency call to the stroke team, provide the patient's location and personal information, allowing patients to get treatment as quickly as possible. Conclusion:An emergency system, which uses the FaceDrop Recognition App, can assist in rapid stroke risk assessment. The system we propose optimizes management.