Predicting Student Depression Using the Naive Bayes Model on the Student Depression Dataset from Kaggle

Abstrak

Background of Study: The increasing prevalence of depression among college students highlights the urgent need for effective early detection strategies to promote mental well-being within higher education environments. Aims and Scope of Paper: This study aims to develop a predictive model for student depression using the Naive Bayes classification algorithm, with a focus on identifying key contributing factors from student-related data. Methods: The research utilizes the Student Depression dataset from Kaggle, containing structured survey data on academic stress, sleep duration, financial stress, GPA, and family mental health history. Data preprocessing included feature selection, handling of missing values, and normalization. The dataset was split into training and testing sets at a 75:25 ratio. Model training was conducted using the R programming language with the application of Laplace smoothing. Result: The Naive Bayes model achieved an accuracy of 77.66%, a specificity of 84.21%, and a sensitivity of 68.42%, indicating strong predictive performance, particularly in identifying depressive cases. Financial and academic stress were identified as the most influential factors. Conclusion: Despite its simplicity, the Naive Bayes algorithm proves to be an effective tool for initial screening of students at risk of depression, offering valuable support for educational institutions in delivering timely mental health interventions.