Which algorithms are used in this teen mental health risk assessment project?

The system implements and compares two algorithms: Logistic Regression (interpretable) and XGBoost (ensemble). XGBoost achieved superior performance with 87.5% accuracy and 0.932 ROC-AUC.

What is the accuracy of the mental health risk assessment model?

The XGBoost model achieves 87.5% accuracy with an F1-Score of 87.6% and ROC-AUC of 0.932, demonstrating strong predictive capability for teen depression risk assessment.

What features are used for teen mental health risk assessment?

The system uses features including daily social media usage, sleep hours, stress level, anxiety level, addiction level, physical activity, and engineered features like sleep_media_ratio and total_risk_score.

Who can benefit from this teen mental health risk assessment system?

School counselors, mental health professionals, healthcare providers, educators, parents, and researchers can use this system for early identification, screening, and intervention planning.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, XGBoost, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

MindGuard - AI-Based Teen Mental Health Risk Assessment System - Complete Project Demo Video

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Machine Learning

MindGuard - AI-Based Teen Mental Health Risk Assessment System

The MindGuard - AI-Based Teen Mental Health Risk Assessment System is a comprehensive machine learning solution that predicts depression risk in teenagers using behavioral and lifestyle data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements XGBoost and Logistic Regression with leak-free SMOTE, achieving 87.5% accuracy and 0.932 ROC-AUC.

The system utilizes a comprehensive dataset of 1,500+ teen mental health records, encompassing variables such as daily social media usage, sleep patterns, stress levels, anxiety levels, and addiction levels. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time depression risk assessment, enabling early intervention and mental health support for adolescents.

Python 3.8+ Machine Learning XGBoost Logistic Regression Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Depression Risk Prediction
XGBoost (87.5% Accuracy)
Logistic Regression (82.7% Accuracy)
Feature Engineering (sleep_media_ratio, total_risk_score)
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time Risk Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with L2 regularization, class_weight='balanced', max_iter=2000

🎯 Accuracy: 82.7%

⚡ XGBoost

Gradient boosting with n_estimators=200, max_depth=6, learning_rate=0.1, regularization

🎯 Accuracy: 87.5%

🔧 Feature Engineering

sleep_media_ratio, activity_media_ratio, screen_to_sleep_ratio, total_risk_score

📊 Top Feature: Stress Level

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV Mean: 92.8%

Methodology & Workflow

1 Data Collection
1,500+ teen mental health records with 15+ features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Logistic Regression and XGBoost with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time risk assessment

Model Performance Comparison

Metric	Logistic Regression	XGBoost	Best
Test Accuracy	82.7%	87.5%	XGBoost
Test Precision (Weighted)	82.5%	88.0%	XGBoost
Test Recall (Weighted)	82.7%	87.2%	XGBoost
Test F1-Score (Weighted)	82.3%	87.6%	XGBoost
Test ROC-AUC	89.2%	93.2%	XGBoost
CV Mean ROC-AUC	88.4%	92.8%	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Teen Mental Health Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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