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

Jaehak Yu, Sejin Park, Hansung Lee, Cheol-Sig Pyo and Yang Sun Lee
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Jaehak Yu: Department of KSB (Knowledge-converged Super Brain) Convergence Research, ETRI (Electronics and Telecommunications Research Institute), Daejeon 34129, Korea
Sejin Park: Research Team for Health & Safety Convergence, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea
Hansung Lee: Computer Engineering, Youngsan University, Kyungnam-do 50510, Korea
Cheol-Sig Pyo: Department of KSB (Knowledge-converged Super Brain) Convergence Research, ETRI (Electronics and Telecommunications Research Institute), Daejeon 34129, Korea
Yang Sun Lee: Division of Convergence Computer and Media, Mokwon University, Daejeon 35349, Korea

Mathematics, 2020, vol. 8, issue 7, 1-16

Abstract: 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%.

Keywords: National Institutes of Health Stroke Scale (NIHSS); health monitoring system; stroke analysis; machine learning; stroke severity prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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