Abstract:
This project presents a machine learning-based approach for depression detection in college
students, aiming to create an innovation that serves as an example of interdisciplinary integration.
By addressing the limitations of traditional methods such as visiting mental health
experts or completing standard questionnaires, the proposed approach offers a more comfortable
and efficient screening process. The model is trained on demographic information,
physical health problems, relationships, and university life aspects, excluding direct mental
health questions. The results demonstrate over 90% prediction accuracy, highlighting the
potential of machine learning-based approaches for depression screening. The success of
the predictive model can be attributed to the integration of knowledge from the fields of
information technology and health science. User satisfaction survey on evaluating the predictive
model as a learning tool for interdisciplinary integration was also conducted. The
study population included a sample of fifty nursing students enrolled at Mae Fah Luang
University. High user satisfaction ratings further support the success of this project.
