Paper Info
Title
Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke
Abstract
This paper shows the simultaneous clustering and classification that is done in order to discover internal grouping on an unlabeled data set. Moreover, it simultaneously classifies the data using clusters discovered as class labels. During the simultaneous clustering and classification, silhouette and F-1 scores were calculated for clustering and classification, respectively, according to the number of clusters in order to find an optimal number of clusters that guarantee the desired level of classification performance. In this study, we applied this approach to the data set of Ischemic stroke patients in order to discover function recovery patterns where clear diagnoses do not exist. In addition, we have developed a classifier that predicts the type of function recovery for new patients with early clinical test scores in clinically meaningful levels of accuracy. This classifier can be a helpful tool for clinicians in the rehabilitation field.
Year
DOI
Venue
2020
10.1166/jmihi.2020.3061
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
Keywords
DocType
Volume
Unsupervised Learning,Simultaneous Clustering and Classification,Ischemic Stroke,Function Recovery
Journal
10
Issue
ISSN
Citations 
6
2156-7018
0
PageRank 
References 
Authors
0.34
0
20
Name
Order
Citations
PageRank
Hyungtai Kim100.34
Minhee Lee200.34
Min Kyun Sohn300.34
Jong-Min Lee411123.54
Deog Yung Kim500.34
Sam-Gyu Lee600.34
Yong-Il Shin711.42
Gyung-Jae Oh800.34
Yang Soo Lee901.01
Min Cheol Joo1000.34
So Young Lee11364.97
Junhee Han1200.34
Jeonghoon Ahn1300.34
Won Hyuk Chang1441.44
Ji Yoo Choi1500.34
Sung Hyun Kang1600.34
Dong Han Lee1700.34
Young Taek Kim1800.34
Mun-Taek Choi1900.34
Yunhee Kim20464.66