To Explore Intracerebral Hematoma with a Hybrid Approach and Combination of Discriminative Factors
| Autor: | Hui-Chu Chiu, Yao-Hsien Lee, Deng-Yiv Chiu, Chen-Shu Wang, Wen-Chih Chang, Ming-Hsiung Ying, Chih-Cheng Wang, Chi-Chung Lee, Mei-Yu Wu |
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| Rok vydání: | 2016 |
| Předmět: |
020205 medical informatics
Computer science Decision tree Health Informatics 02 engineering and technology Machine learning computer.software_genre Fuzzy logic 03 medical and health sciences 0302 clinical medicine Fuzzy Logic Health Information Management Discriminative model 0202 electrical engineering electronic engineering information engineering Humans Cerebral Hemorrhage Advanced and Specialized Nursing Hematoma business.industry Decision Trees Centroid Pattern recognition Models Theoretical Intracerebral hematoma Support vector machine Artificial intelligence business Classifier (UML) computer Algorithms 030217 neurology & neurosurgery Test data |
| Zdroj: | Methods of Information in Medicine. 55:450-454 |
| ISSN: | 2511-705X 0026-1270 |
| DOI: | 10.3414/me15-01-0137 |
| Popis: | SummaryObjectives: To find discriminative combination of influential factors of Intracerebral hematoma (ICH) to cluster ICH patients with similar features to explore relationship among influential factors and 30-day mortality of ICH. Methods: The data of ICH patients are collected. We use a decision tree to find discriminative combination of the influential factors. We cluster ICH patients with similar features using Fuzzy C-means algorithm (FCM) to construct a support vector machine (SVM) for each cluster to build a multi-SVM classifier. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality. Results: The two influential factors chosen to split the decision tree are Glasgow coma scale (GCS) score and Hematoma size. FCM algorithm finds three centroids, one for high danger group, one for middle danger group, and the other for low danger group. The proposed approach outperforms benchmark experiments without FCM algorithm to cluster training data. Conclusions: It is appropriate to construct a classifier for each cluster with similar features. The combination of factors with significant discrimination as input variables should outperform that with only single discriminative factor as input variable. |
| Databáze: | OpenAIRE |
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