Skip to main content
Fig. 2 | BMC Neurology

Fig. 2

From: Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis

Fig. 2

Starting from the bottom, the clusters are progressively joined (at levels of similarity shown at their union) until a single cluster is formed at the top (A). NbClust provides the statistically optimum number of clusters, which were three for the index of 6:24 indicators (B). Cluster 1 (n = 45) shows the highest rate of subarachnoid hemorrhage (SAH) (34/45, 75.6%); Cluster 2 (n = 110) shows a moderate risk of SAH (42/110, 38.2%); and cluster 3 (n = 505) shows a relatively mild risk of SAH (89/505, 17.6%). Consequently, the cluster variable is significantly associated with the risk of SAH (C)

Back to article page

BMC Neurology

ISSN: 1471-2377

Contact us