Article
MENTAL WELLNESS MONITORING SYSTEM FOR ENTREPRENEURIAL STUDENTS
Mental health issues including anxiety, stress, and depression are becoming more common among college students, especially those who are under pressure from their studies and careers. Early and precise detection of depressed tendencies can facilitate prompt intervention and psychological assistance. In this work, we use structured survey data to propose a Cat Boost-based machine learning model for predicting depression levels among college students. Age, gender, academic year, CGPA, and university background are among the demographic variables included in the dataset, along with psychological markers obtained from the PHQ-9 survey. A cleaned dataset of 2,028 samples with six categories of depression—No Depression, Minimal, Mild, Moderate, Moderately Severe, and Severe Depression—was used to train and assess the model. Because it can handle categorical variables natively and capture intricate non-linear correlations without requiring a lot of preprocessing, Cat Boost was chosen. The experimental findings showed an accuracy of more than 92%, surpassing conventional machine learning baselines and attaining performance on par with or superior to deep learning systems like FE-BiON. Extensive analyses, such as confusion matrix evaluation and feature importance visualization, verify that PHQ-9-related items have a considerable impact on prediction outcomes. The suggested technique provides a dependable and comprehensible framework for automated depression screening, assisting academic institutions and mental health specialists in fostering the wellbeing of students.
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