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학술대회 Semantic Analysis of NIH Stroke Scale using Machine Learning Techniques
Cited 11 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
유재학, 김다미, 박홍규, 천승철, 조강희, 김선진, 유성규, 박세진, 홍승희
International Conference on Platform Technology and Service (PlatCon) 2019, pp.82-86
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.
KSP 제안 키워드
Adults and elderly, C4.5 Algorithm, Classification and prediction, Decision Tree(DT), Early Detection, Elderly People, Execution mechanism, Feature reduction, Information gain(IG), Machine Learning technique(MLT), Rapid detection