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Article

An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

1
Department of KSB (Knowledge-converged Super Brain) Convergence Research, ETRI (Electronics and Telecommunications Research Institute), Daejeon 34129, Korea
2
Research Team for Health & Safety Convergence, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea
3
Computer Engineering, Youngsan University, Kyungnam-do 50510, Korea
4
Division of Convergence Computer and Media, Mokwon University, Daejeon 35349, Korea
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(7), 1115; https://doi.org/10.3390/math8071115
Received: 5 June 2020 / Revised: 1 July 2020 / Accepted: 2 July 2020 / Published: 6 July 2020
Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%. View Full-Text
Keywords: National Institutes of Health Stroke Scale (NIHSS); health monitoring system; stroke analysis; machine learning; stroke severity prediction National Institutes of Health Stroke Scale (NIHSS); health monitoring system; stroke analysis; machine learning; stroke severity prediction
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MDPI and ACS Style

Yu, J.; Park, S.; Lee, H.; Pyo, C.-S.; Lee, Y.S. An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale. Mathematics 2020, 8, 1115. https://doi.org/10.3390/math8071115

AMA Style

Yu J, Park S, Lee H, Pyo C-S, Lee YS. An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale. Mathematics. 2020; 8(7):1115. https://doi.org/10.3390/math8071115

Chicago/Turabian Style

Yu, Jaehak, Sejin Park, Hansung Lee, Cheol-Sig Pyo, and Yang S. Lee 2020. "An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale" Mathematics 8, no. 7: 1115. https://doi.org/10.3390/math8071115

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