Data-driven depression prediction: insights from a machine learning challenge

Abstract

Depression is a prevalent mental health condition that significantly impacts quality of life. Traditional diagnostic methods rely on clinical assessments and self-reported questionnaires, which can be subjective and time-consuming. The increasing availability of digital health data presents an opportunity for automated and data-driven depression screening using machine learning techniques. While previous research has demonstrated the feasibility of AI-driven depression detection, challenges remain in feature selection, handling imbalanced datasets, and ensuring model interpretability. This study develops a machine-learning model using structured questionnaire data to predict depression, addressing these challenges and improving screening accuracy. The paper discusses relevant literature, dataset preprocessing, model selection, results, and future directions, concluding with insights and implications of our findings.