Overview
This project aims to provide an initial assessment of depression risk through an easy-to-use online application. It utilizes machine learning algorithms to predict the severity of depression based on user-provided questionnaire responses.
Backend: Python (Machine Learning Algorithms)
Web Framework: Flask (Deployment)
Frontend: HTML, CSS, JavaScript (User Interface)
Machine Learning: Decision Trees, Random Forests
User-Friendly Interface: Accessible via any internet-connected device.
Comprehensive Questionnaire: Covers physical and mental health aspects relevant to depression.
Depression Classification: Utilizes Random Forest Algorithm to classify users into five categories: Not Depressed, Mildly Depressed, Moderately Depressed, Severely Depressed, and Critically Depressed.
Recommendations: Encourages seeking professional help for diagnosed cases of depression.
The application is deployed using Flask, allowing seamless interaction between the user interface and the machine learning models.
User Input: Users complete the questionnaire detailing their current mental and physical state.
Prediction: Based on the input, the system predicts the severity of depression.
Outcome: Users receive their classification and are encouraged to seek professional help if necessary.
This platform serves as an initial screening tool and does not substitute professional medical advice. It aims to raise awareness and prompt further evaluation by healthcare professionals.
- Integration with additional machine learning models for improved accuracy.
- Enhanced user experience features based on feedback and usage analytics.
- Contributing
- Contributions are welcome. Please fork the repository, create a branch, and submit a pull request with your enhancements.