神经网络识别健康风险个体
Neural network to identify individuals at health risk
摘要：
适当的体育活动可以降低心脏病和高血压等疾病的风险。然而，尽管大多数人声称进行一些体育锻炼，但只有少数人的锻炼足以保持健康的生活方式。因此，在过去十年中，缺乏身体活动已成为公众健康的主要问题之一。研究表明，从高中到大学，体育活动的减少幅度最大。因此，快速识别因缺乏身体活动而面临健康风险的大学生至关重要。研究还表明，一个人的身体活动水平与种族和性别等人口特征以及自我激励和家人和朋友的支持高度相关。这些信息可以通过20分钟的问卷从每个学生那里收集，但是分发和分析每个问卷所需的时间在大学校园里是不可行的。因此，我们提出了一个自动识别的学生处于危险之中，使这些学生可以更容易地有针对性的大学校园和体育活动促进部门。我们在本文中提出了一个监督的反向传播多层神经网络的初步结果，学生分为风险或不处于危险之中。
英文摘要：
The risk of diseases such as heart attack and high blood pressure could be reduced by adequate physical activity. However, even though majority of general population claims to perform some physical exercise, only a minority exercises enough to keep a healthy living style. Thus, physical inactivity has become one of the major concerns of public health in the past decade. Research shows that the highest decrease in physical activity is noticed from high school to college. Thus, it is of great importance to quickly identify college students at health risk due to physical inactivity. Research also shows that the level of physical activity of an individual is highly correlated to demographic features such as race and gender, as well as self motivation and support from family and friends. This information could be collected from each student via a 20 minute questionnaire, but the time needed to distribute and analyze each questionnaire is infeasible on a collegiate campus. Thus, we propose an automatic identifier of students at risk, so that these students could easier be targeted by collegiate campuses and physical activity promotion departments. We present in this paper preliminary results of a supervised backpropagation multilayer neural network for classifying students into at-risk or not at-risk group.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
