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%.

中文翻译：

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基于机器学习和深度分析技术的NIH卒中规模的老年人健康监测系统

近来，随着老龄化社会的快速变化和对医疗保健的日益增长的兴趣，通过各种医疗保健设备和服务进行疾病预测和管理受到了广泛的关注。特别地，以脑血管疾病为代表的中风是非常危险的疾病，其中成年人和老年人的死亡或精神和身体后遗症很大。这种中风疾病的后遗症非常危险，因为它们使社会和经济活动变得困难。在本文中，我们提出了一种基于国立卫生研究院卒中量表（NIHSS）来预测和深入分析65岁以上老年人卒中严重程度的新系统。此外，我们使用C4.5决策树算法，这是一种预测和分析机器学习技术的方法。C4。5个决策树是机器学习算法，可提供执行机制和语义解释分析的其他深入规则。最后，本文证明了C4.5决策树算法可用于分类和预测卒中严重程度，并获得额外的NIHSS特征减少效果。因此，在实际系统运行期间，所提出的模型仅使用18个笔划比例尺特征中的13个特征（包括年龄），从而可以提供更快，更准确的服务支持。实验结果表明，使用C4.5决策树算法，该系统可通过减少患者NIH卒中量表的测量时间并提高操作效率（总体准确性）来实现这一目标，从而达到91.11％。