Predicting Student Depression and Stress Using Multimodal Daily Behavioral Data

Abstract

This study investigates the possibility of forecasting depression, stress and mood in college students, through the examination of their activities. The dataset comprises. Subsequent depression assessments (PHQ-9) daily step counts, sleep habits, screen time social media engagement, communication records, movement data and students self-reported mood and stress states. A Random Forest Regression technique was applied to estimate PHQ-9 scores while Random Forest Classification categorized students into Mild, Moderate and Severe depression groups. Addi-tional models were created to forecast stress levels (1-5) mood ratings (0-10) and total risk (Low/High).We can have resulted to show that factors like duration of sleeping, screen timing, motion and social well-being, communication frequency are very important indicator of mental health changes. This approach can be useful, non-intrusive way to monitor students mental health and well-being for providing support when needed.