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Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke
- Simultaneous Clustering and Classification of Function Recovery Patterns of Ischemic Stroke
- Kim, Hyungtai; Lee, Minhee; Sohn, Min Kyun; Lee, Jongmin; Kim, Deog Yung; Lee, Sam-Gyu; Shin, Yong-Il; Oh, Gyung-Jae; Lee, Yang-Soo; Joo, Min Cheol; Lee, So Young; Han, Junhee; Ahn, Jeonghoon; Chang, Won Hyuk; Choi, Ji Yoo; Kang, Sung Hyun; Lee, Dong Han; Kim, Young Taek; Choi, Mun-Taek; Kim, Yun-Hee
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
- JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS vol. 10, no. 6, pp. 1401 - 1407
- Unsupervised Learning; Simultaneous Clustering and Classification; Ischemic Stroke; Function Recovery
- AMER SCIENTIFIC PUBLISHERS
- SCIE; SCOPUS
- Document Type
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- 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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