Authors - Amol Dhumane, Jitendra Chavan, Arijit Dutta, Priyanka Paygude, Aditi Sharma, Datta Takale, Yashwant Dongre Abstract - Depression is a psychiatric condition that is largely common all over the world and greatly influences the emotional stability, cognitive performance and behavior functioning. Computational techniques that can detect the condition early can help to prevent psychological dangers in the long term and ensure timely treatment of the disease. This paper refers to a complete machine learning architecture of automated depression recognition of textual information based on hybrid feature engineering and ensemble learning approaches. The suggested methodology is a combination of text preprocessing, Term Frequency / Inverse Document Frequency (TF -IDF) vectorization, unigram and bigram features, hand-crafted statistics and sentiment-based indicators, and several classification models such as Logistic Regression, Random Forest, XGBoost, and LightGBM. The issue of class imbalance is tackled using Synthetic Minority Over-sampling Technique (SMOTE) and compared. The original dataset of 7,489 samples was cleaned and narrowed down to 7,486 valid cases. Accuracy, Precision, Recall, F1 score, ROC-AUC and 5-fold cross-validation were used to evaluate the performance. There are experimental results to show that ensemble models are more effective compared to traditional linear classifiers. XGBoost performed best in the overall performance of 94.59% accuracy and F1-score of 0.8323. The hybrid-based feature fusion technique has a considerable improvement on the classification performance and does not sacrifice the level of interpretability and computational efficiency, which is why the framework is applicable to scalable mental health analytics services.
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