Abstract:
Academic stress among university students is receiving widespread attention in
today's academic environment. Academic stress arises from the myriad demands and
challenges inherent in academic life, the management of which is fundamental to
students' overall health and well-being. University students face a high sensitivity to
academic stress and need proactive strategies to reduce stress. There is growing
evidence that certain food choices can positively impact academic stress, but the exact
link varies among individuals and depends on the specific foods consumed. This
research project developed a prediction model using a machine-learning algorithm to
determine the beneficial effects of dietary decisions on academic stress among
students at the University of Ruhuna. The main goals are to determine the stress levels
of the students, comprehend how they eat when under stress, and pinpoint foods that
help reduce stress. The study combines supervised and unsupervised learning
techniques using a two-pronged design. A dataset of 597 student participants, and a
K-means algorithm are employed in the field of unsupervised learning to intelligently
classify students into different stress levels based on their replies. This process
revealed complicated patterns of food consumption. Simultaneously, supervised
learning, facilitated by the K-Nearest Neighbors (KNN) algorithm, creates
correlations between stress levels and personalized food consumption habits. The
study concluded that there was a noteworthy pattern among students at University of
Ruhuna with high stress levels, who consumed an average of 2.25–2.50 times more
sweet foods than spicy and milky foods than their low-stress counterparts, who
consumed an average of 1.00–1–25 times sweet foods. It illustrates the connection
between a person's food intake and stress levels, as well as how eating well can
temporarily reduce stress. These findings have important consequences for nutritional
therapies that might be used to improve the ability to manage stress among students
at University of Ruhuna.
