Early Detection and Prediction of Depression Based on Data-Driven Machine Learning Techniques in Mental Healthcare

Depression is a significant public health concern, as it is among the causes of the burden of disease in the world. Both genetic and environmental factors define whether one is at risk of becoming depressed or not. Although genetic influences cannot be changed, it is vital to find out possible reversible environmental factors and make an attempt to restrict the manifestation of depression. As a timely intervention and effective mental care, it is important to identify cases of depression as early as possible. In this work, the authors provide a machine learning (ML) predictive model of depression severity as applied to the DASS (Depression Anxiety Stress Scales) dataset. The LSTM (Long Short-Term Memory) model and XGBoost (Extreme Gradient Boosting) were developed and tested with better results on capturing sequential patterns and crucial emotional features. A lot of pre-processing of data, feature selection, and class balancing methods, such as SMOTE (Synthetic Minority Over-sampling) and random oversampling, were used to increase the reliability of models. The LSTM model got 99.73% in accuracy, precision, recall and F1-score, whereas XGBoost got 99.48% as compared to the baseline models, namely BERT (Bidirectional Encoder Representations from Transformers), Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR) and Naïve Bayes (NB). The findings support the effectiveness of the suggested methodology in effective, consistent, and almost perfect depression classification, which contributes to its possible use in the field of practice as a mental health assessment and early intervention.