AI-BASED APPROACH FOR EARLY DIAGNOSIS SUPPORT IN HEMORRHAGIC STROKE

A hemorrhagic stroke is a life-threatening medical condition that happens when a blood vessel in your brain ruptures and bleeds.It constitutes a burden on health services and the victim's family. The current definitive diagnosis of stroke is based on brain scanning. However, the clinical diagnosis of hemorrhagic stroke is complex and depends on the skills and experience of the practitioner. Human diagnostic errors lead to delays in treatment and thus compromise clinical outcomes. Our vision is to propose an artificial intelligence approach for medical assistance in the early clinical diagnosis of hemorrhagic strokes. We studied and compared three machine learning models, namely logistic regression, Random Forest and artificial neural networks, to choose the best one after setting up a stroke dataset and identifying the most important characteristics. We can conclude that our system designed with artificial intelligence is important with satisfactory results to help health workers make the rapid diagnosis of hemorrhagic stroke and promote rapid treatment of suspected patients.