PHQ-9 Student Depression Dataset
Description
The PHQ-9 Student Depression Dataset contains responses from 250 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions. The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories: Minimal (0-4) Mild (5-9) Moderate (10-14) Moderately Severe (15-19) Severe (20-27) This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include: Sentiment Analysis: Analyzing emotional tones in text responses to assess depression. Text Classification: Classifying responses into different depression severity levels. Predictive Modeling: Predicting depression severity based on textual responses. Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression. The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources. When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including: Data Privacy: Anonymizing personal information. Informed Consent: Ensuring participants give consent before data collection. Support Resources: Providing support for individuals who may exhibit serious mental health concerns. Applications: Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques. AI in Healthcare: It can be used in the development of tools for automated mental health assessment. Education: Training students or professionals in recognizing depression symptoms and analyzing responses.