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Classification methods

SL Seulkee Lee
SK Seonyoung Kang
YE Yeonghee Eun
HW Hong-Hee Won
HK Hyungjin Kim
HC Hoon-Suk Cha
EK Eun-Mi Koh
JL Jaejoon Lee
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We used divisive hierarchical cluster analysis [23] to identify subgroups of patients with similar characteristics. Hierarchical clustering methods are categorized into agglomerative (bottom-up) and divisive (top-down) procedures. Divisive procedures begin by considering a group that includes all samples, which is divided into two groups in subsequent stages until all groups comprise only a single sample [24]. We used Euclidean distances to calculate the dissimilarities between observations. The optimum number of clusters was determined according to the average silhouette width [25, 26].

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