A machine learning approach for predicting the depression status among undergraduates: A case study from Sri Lanka

Abstract

Abstract Background Depression is a common mental health disorder that affects a significant portion of the global population. In Sri Lanka, depression is a growing concern, especially among undergraduate students. This study aims to develop a machine learning model to predict the depression status of science undergraduates from a state university in Sri Lanka Methods Data was collected from a sample of 363 undergraduates via a Google form which included the Patient Health Questionnaire-9 (PHQ-9) and several questions pertaining to their background. The data collected (through a convenience sampling scheme) was then used to build and evaluate 13 different machine learning models. The accuracy, precision and recall were used to evaluate the performance of each model. Results 30% of the sample consisted of those who screened positive for depression. The results showed that the Gradient Boosting Classifier with an accuracy of 79% on the test set had the best accuracy as well as the best precision in predicting the depression status. Feature importance analysis identified the overall life satisfaction and level of stress associated with academic activities as the most important predictors of depression. Furthermore, the extent to which the respondents enjoy their university life, their BMI and whether or not they had nutritionally balanced diets were found to be the next three most important factors of depression status. Conclusions This study is possibly the first attempt to model and predict the depression status of Sri Lankan undergraduates using machine learning approaches. The developed model can be used as a screening tool to identify students who may be at risk of developing depression and can aid in providing targeted interventions to improve the mental health and well-being of undergraduates.