Decision Tree-Based Approach for Predicting Mental Health Symptoms in University Students: A Case Study at IT Del

Abstract

Mental health plays a critical role in ensuring the well-being and productivity of university students. Institut Teknologi Del students encounter multiple stressors, including academic demands, dormitory life, and social interactions. This study proposes the development of a web-based predictive system to enable students to self-assess their mental health status using the Decision Tree algorithm. The dataset comprises 1,457 psychological screening records from IT Del students, categorized into two primary labels: Tend to Be Depressed and Tend to Be Anxious. The system architecture employs Python with Flask for the backend, Laravel for the frontend, and integration via a REST API. The development process follows the CRISP-DM methodology, while model performance is evaluated using a confusion matrix. For the Tend to Be Depressed category, the model achieved an accuracy of 0.931; precision: 0.931; recall: 0.931; F1-score: 0.931. For the Tend to Be Anxious category, performance reached accuracy 0.839; Precision 0.921; Recall 0.782; F1-score 0.846. With its straightforward yet effective model design, the proposed system offers a practical and accurate early detection tool for students’ mental health conditions. Furthermore, it has the potential to support campus counseling services in a digital, scalable, and sustainable manner