Neural network to identify individuals at health risk

TL;DR

This paper proposes a neural network model to quickly identify college students at health risk due to physical inactivity, enabling targeted health interventions.

Contribution

It introduces a neural network-based method for classifying students at health risk using demographic and motivational data from a brief questionnaire.

Findings

Preliminary results show effective classification accuracy.

Neural network approach outperforms traditional methods.

Potential for scalable health risk screening in colleges.

Abstract

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.