Machine Learning-Based Approaches for Early Prediction of Depression

Depression is a widespread and incapacitating mental health illness which continues to pose problems for the world's healthcare systems. Effective therapy depends on prompt discovery and intervention, yet the difficulty of detecting depression frequently results in underdiagnosis and undertreatment. In this study, a thorough overview of the use of machine learning (ML) models for accurate depression diagnosis is provided. The primary goal of this research is to improve early diagnosis, risk assessment, and patient care in the field of depression detection utilizing predictive models. For this purpose, the Depression Prediction dataset available on Kaggle was utilized, and ten different ML models were employed. The results showed that Random Forest outperformed the rest of the models by achieving 98.22% accuracy while Ensemble method obtained 0.98 F1-score.