Application of Random Forest in Modeling the Prevalence of Depression among Murang’a University of Technology Students

Abstract

Around the world, depression is a prevalent mental illness and it affects the way people think, feel, talk and conduct their daily activities. The associated stigma often leads to misdiagnosis, posing risks such as disability and suicide. The study employed random forest algorithm to model the prevalence of depression among Murang’a University of technology (MUT) students. A sample of 1448 students from the different schools at the university participated in the study by completing questionnaires on sociodemographic and other factors associated with depression. The questionnaires were administered through social media platforms. Participants were selected using proportionate stratified random sampling and simple random sampling to ensure that a representative sample was chosen from each school. The data gathered was examined using descriptive and inferential statistics. Depression was measured using the Patient Health Questionnaire scale (PHQ-9). Using a cut-off point of 10, 25.97% students had depressive symptoms. This comprised of 19.61% moderate symptoms and 6.35% severe symptoms. The confusion matrix criteria were used to assess the performance of random forest in modeling depression prevalence among MUT students. Metrics for random forest included, accuracy (0.9868), sensitivity (0.95), specificity (1.00), positive predictive value (1.00), and negative predictive value (0.9824). Implementing targeted interventions founded on identified risk and protective factors and exploring the long-term outcomes of these interventions would contribute to the evolving field of mental health research within academic settings.