Abstract:

Depression is a debilitating condition that adversely affects individuals' daily lives, with a growing number of people experiencing long-term symptoms worldwide. Detecting depression at an early stage is crucial for timely intervention and treatment, yet it remains a significant challenge for psychiatrists.

Recent advancements in Natural Language Processing (NLP) have enabled researchers to analyze text content on social media to identify signs of depression. This study reviews various machine learning techniques used in prior research to detect depression and addresses the limitations related to model representation and accuracy.

The present work proposes a solution by employing a combination of machine learning algorithms, specifically K-Nearest Neighbors (KNN), XGBoost, Decision Tree, and Random Forest, to enhance text representation and model performance in detecting depression.

Each algorithm offers distinct advantages: KNN for its simplicity, XGBoost for its gradient boosting efficiency, Decision Tree for interpretability, and Random Forest for its robust ensemble approach. Implemented on real-world datasets, this hybrid approach demonstrates superior accuracy compared to existing state-of-the-art methods in detecting depression, highlighting the potential of these techniques in early identification and intervention.

Keywords: Dataset, Machine Learning Algorithm knearst neighbor, Xgboost, Decision Tree, Random forest

The process typically involves several key stages:

1. Data Collection: Gathering textual data from various social media platforms, often by using APIs or web scraping methods, while ensuring adherence to privacy and ethical considerations.

2. Data Preprocessing: Cleaning and transforming the raw text data to remove noise, irrelevant information, and personal identifiers, while retaining essential linguistic features.

3. Feature Extraction: Extracting relevant features from the preprocessed textual data, such as word frequencies, sentiment scores, emotional expressions, linguistic style, and other contextual attributes.

4. Model Training: Using the labelled dataset, a machine learning classifier is trained to identify patterns in the textual data that are connected to depression and emotional discomfort.

5. Model Evaluation: determining the accuracy, precision, recall, and F1 score of the trained model to evaluate its performance, ensuring the model's dependability in spotting depression.

6. Deployment: Implementing the trained model to automatically analyze real-time social media posts and classify them as either indicative of a potential depression or not. The application of depression detection