NEURAL NETWORK TO IDENTIFY INDIVIDUALS AT HEALTH RISK

The risk of diseases such as heart attack and high blood pressure could be reduced by adequate physicalactivity. 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 oneof the major concerns of public health in the past decade. Research shows that the highest decrease inphysical activity is noticed from high school to college. Thus, it is of great importance to quickly identifycollege students at health risk due to physical inactivity. Research also shows that the level of physicalactivity of an individual is highly correlated to demographic features such as race and gender, as well asself motivation and support from family and friends. This information could be collected from eachstudent via a 20 minute questionnaire, but the time needed to distribute and analyze each questionnaire isinfeasible on a collegiate campus. Thus, we propose an automatic identifier of students at risk, so thatthese students could easier be targeted by collegiate campuses and physical activity promotiondepartments. We present in this paper preliminary results of a supervised backpropagation multilayerneural network for classifying students into at-risk or not at-risk group