Depression Detection System using Python

Introduction

Depression is not just a fleeting emotion but a severe medical condition affecting millions worldwide. With advancements in technology, it’s becoming easier to detect and diagnose depression in its early stages. This article delves into an innovative depression project that employs Python to analyze symptoms and recommend psychiatric help.

Methodology

The system under discussion utilizes various machine learning algorithms and frameworks for a thorough depression detection process. At the core of the project lies the Naive Bayes algorithm, which is implemented for quiz-based detection. This is coupled with Convolutional Neural Networks (CNN) that analyze facial expressions from images and video feeds.

Technology Stack

The project uses Python for backend development, facilitated by the Django framework. HTML, CSS, and JavaScript make up the frontend, and MySQL is employed for database management.

Features

• Quiz-Based Detection: Users can take quizzes to evaluate their mental state. The quiz utilizes the Naive Bayes algorithm to analyze answers and categorize the type of depression—be it anxiety, PTSD, or bipolar disorder.
• Facial Expression Analysis: The system requires users to speak about themselves for one minute while recording their facial expressions. These expressions are then analyzed to detect signs of depression.
• Clinic Recommendations: Upon detection, the system also provides a list of nearby clinics where users can consult a psychiatrist for further diagnosis and treatment.

Advantages

• Self-Testing: The project empowers users to take preliminary self-tests, increasing self-awareness about their mental health.
• Ease of Access: The system makes it convenient for users to find nearby psychiatric help, bridging the gap between detection and treatment.

Conclusion

As mental health takes center stage in healthcare, projects like these demonstrate the utility of combining technology and psychology for enhanced diagnostic accuracy. While this is a significant step in depression detection, it also paves the way for more sophisticated healthcare solutions in the future.

Sample Code

# Import required modules
import random

# Function to administer a quiz to the user
def administer_quiz():
    questions = [
        "Do you often feel sad or down?",
        "Do you struggle with anxiety?",
        "Do you have mood swings?",
        "Do you feel hopeless?",
        "Do you find it hard to concentrate?"
    ]
    
    score = 0
    
    for question in questions:
        print(question)
        answer = input("Yes or No: ").lower()
        
        if answer == 'yes':
            score += 1
    
    return score

# Function to determine the type of depression
def determine_depression(score):
    if score == 0:
        return "No signs of depression."
    elif score < 3:
        return "Mild symptoms of anxiety."
    elif score < 5:
        return "Moderate symptoms, possibly bipolar disorder."
    else:
        return "Severe symptoms, consult a psychiatrist immediately."

# Main function
def main():
    print("Welcome to the Depression Detection System.")
    
    consent = input("Would you like to take a depression detection quiz? (Yes or No): ").lower()
    
    if consent == 'yes':
        score = administer_quiz()
        result = determine_depression(score)
        print(result)
    else:
        print("Okay, take care.")

# Run the program
if __name__ == "__main__":
    main()

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