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Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke

Title
Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke
Authors
Kim, HyungtaiLee, MinheeSohn, Min KyunLee, JongminKim, Deog YungLee, Sam-GyuShin, Yong-IlOh, Gyung-JaeLee, Yang-SooJoo, Min CheolLee, So YoungHan, JunheeAhn, JeonghoonChang, Won HyukChoi, Ji YooKang, Sung HyunLee, Dong HanKim, Young TaekChoi, Mun-TaekKim, Yun-Hee
Ewha Authors
안정훈
SCOPUS Author ID
안정훈scopus
Issue Date
2020
Journal Title
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
ISSN
2156-7018JCR Link

2156-7026JCR Link
Citation
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS vol. 10, no. 6, pp. 1401 - 1407
Keywords
Unsupervised LearningSimultaneous Clustering and ClassificationIschemic StrokeFunction Recovery
Publisher
AMER SCIENTIFIC PUBLISHERS
Indexed
SCIE; SCOPUS WOS
Document Type
Article
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.
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DOI
10.1166/jmihi.2020.3061
Appears in Collections:
신산업융합대학 > 융합보건학과 > Journal papers
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