Exploring Depression Analysis Using Machine Learning: A Comprehensive Approach

Depression is a mental health condition that affects millions of people worldwide. Early diagnosis and intervention can significantly improve the quality of life for those suffering from it. In this blog, I will walk you through my recent project, where I applied machine learning techniques to build a depression analysis system that aids in identifying individuals who might be at risk for depression.

Objective

The primary objective of this project was to develop a machine learning-based model capable of predicting depression levels based on various user inputs. The project also aimed to provide an interactive dashboard where users could input their data and receive insights into their mental health status.

Data Collection

To create the model, I used a publicly available dataset that included various features like social media activity, sleep patterns, and physical activity levels, which are known to have correlations with mental health. The dataset was preprocessed, cleaned, and divided into training and test sets for model evaluation.

Model Development

For model development, I employed several machine learning algorithms, including:

1. Support Vector Machine (SVM)

2. Random Forest

3. Multilayer Perceptron (MLP)

I also combined multiple models to create an ensemble model to improve prediction accuracy. The ensemble approach tends to work well in capturing different patterns from various models, resulting in better performance.

Here are the accuracies of the models:

The ensemble model slightly outperformed the individual models, achieving an accuracy of 93.22%, which indicates that combining multiple models often yields the best results.

Deployment

Once the model was trained and tested, I deployed it using Streamlit, a popular Python library for creating interactive web applications. I built two main components for this project:

1. The Dashboard: A user-friendly interface where individuals can input their symptoms or behavioral data to receive predictions on their mental health. You can check out the live dashboard here: Depression Analysis Dashboard.

2. The Model Implementation: A backend system where the actual machine learning model runs and processes the input data to make predictions. You can test out the live model implementation here: Depression Prediction Model.

code base :- https://github.com/venkatesh-hyper/Depression_analyser

Features of the Dashboard

The dashboard provides a simple and intuitive interface for users to interact with. Key features include:

• Data Input: Users can enter their symptoms and relevant details about their daily life activities.

• Instant Prediction: After submitting the data, users will receive an instant prediction about their mental health status.

• Visualization: The system includes visualizations to help users understand the relationship between various inputs and their predicted mental health status.

Challenges Faced

• Data Quality: One of the biggest challenges in this project was ensuring the quality of the data. Missing values and noisy data can degrade the model's performance. I used techniques like imputation and outlier removal to improve the dataset's quality.

• Feature Selection: Another challenge was selecting the right features. While we had many variables to choose from, it was important to focus on those that had the highest correlation with depression symptoms.

Conclusion

This project allowed me to combine my knowledge of machine learning with real-world applications to address a pressing social issue. By providing a tool for early detection of depression, this system can help in taking timely action, potentially saving lives. The deployment of the system using Streamlit made it easy to share the model with a wider audience, and I hope it can contribute to a better understanding of mental health in the future.

I encourage you to explore the dashboard and model implementation and test the system with your own inputs.