Abstract—Stroke happens when a clot blocks the blood supply to a region of the brain (ischemic stroke) or when an artery ruptures or spills blood (hemorrhagic stroke). Seeking medical care after a stroke may increase one’s chances of survival and reduce long-term brain damage. Neuroimaging helps determine who and how to treat, although it is costly, not always accessible, and may have contraindications. These constraints lead to these reperfusion treatments being underutilized. Using a blood biomarker panel capable of consistently differentiating between ischemic stroke and intracerebral hemorrhage might be very beneficial and straightforward to deploy. Therefore, this study describes a system to speed and improve stroke diagnosis. Using four machine learning algorithms: Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), K-Nearest Neighbor (KNN), and Decision Tree (DT), we aim to find promising blood biomarker candidates for differential stroke diagnosis. A two-stage binary classifier model was created to classify the stroke group vs. the normal group and then categorize the instances allocated to the stroke group into ischemic and hemorrhagic groups. Our findings reveal that SVM is better than ANN, ANFIS, and DT for distinguishing strokes in Egyptian patients, according to our data. The most important blood features are Absolute (ABS) Neutro, Creatine Phosphokinase (CPK), Neutro/Neutrophils, and White Blood Cell (WBC) Count/Leukocytes laboratory tests that may serve as crucial and significant indications for stroke diagnosis. The selected characteristics and a two-stage binary classifier discriminated with higher accuracy (Ischemic and hemorrhagic patients). This method for identifying and classifying brain strokes was accurate, easy to use, and cost-effective.
 
Keywords—machine learning, blood biomarker, stroke, hemorrhagic, ischemic, identification and classification


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