Classification of Stroke Using K-Means and Deep Learning Methods
Abstract
Stroke is a disease caused by blockage or rupture of blood vessels in the brain due to disruption of blood flow, where the blood supply to an area of the brain is suddenly interrupted. This study discusses stroke classification using the K-Means and Deep Learning methods. This study aims to segment patient data to produce patient class labels and classify the results of grouping the data to test the performance of the classification algorithm used. The 4,906 patient data used in this study were grouped using the K-Means method into multiple clusters, including 2 clusters, 3 clusters, 4 clusters, and 5 clusters, and the data grouping findings will be classified. The cluster validation method is the Davies Bouldin Index and the Silhouette Index, while the algorithm used in the classification process is the Deep Learning Algorithm. The classification results produce the most excellent accuracy value in the number of clusters tested, namely 2 clusters of 99.71%.
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