Semantic Analysis of NIH Stroke Scale using Machine Learning Techniques
| Autor: | Seunghee Hong, Seung-Chul Chon, Se-Jin Park, Sunjin Kim, Sungkyu Yu, Damee Kim, Kang Hee Cho, Hongkyu Park, Jaehak Yu |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Semantic interpretation Semantic analysis (machine learning) Decision tree 020207 software engineering 02 engineering and technology medicine.disease Semantics Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Analytics 0202 electrical engineering electronic engineering information engineering medicine Feature (machine learning) cardiovascular diseases Artificial intelligence business Construct (philosophy) Stroke computer 030217 neurology & neurosurgery |
| Zdroj: | 2019 International Conference on Platform Technology and Service (PlatCon). |
| DOI: | 10.1109/platcon.2019.8668961 |
| Popis: | In particular, stroke is a major disease leading to death in adults and elderly people, as well as disability. Rapid detection of stroke is very difficult because the cause and cause of the onset are different for each individual. In this paper, we design and implement a system for semantic analysis of early detection of stroke and recurrence of stroke in Koreans over 65 years old, based on the National Institutes of Health (NIH) Stroke Scale. Using C4.5 of the decision tree series represented by the analytics algorithm of machine learning technique, we conduct a semantic interpretation that analyzes and extracts the semantic rules of the execution mechanism that are additionally provided by C4.5. The C4.5 algorithm is used to construct a classification and prediction model using the information gain of the NIH stroke scale features, and to obtain additional NIH Stroke Scale feature reduction effects. |
| Databáze: | OpenAIRE |
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