Building a Mental Health Prediction System with Machine Learning, SHAP & Flask

说明与用途

构建了一个使用AdaBoost、PHQ‑9、SHAP和Flask的心理健康风险评估Web应用，使用Docker容器化并实现自动监控部署。

问题

Develop an end‑to‑end mental‑health assessment tool that combines ML risk prediction, PHQ‑9 screening, explainability, and a web interface, moving beyond a notebook experiment.

结果

The project delivers a deployable mental‑health risk assessment web app with explainable predictions, PHQ‑9 integration, and support features, demonstrating a complete ML lifecycle from data to production.

工作流

• Collect and preprocess PHQ‑9 data
• Train multiple classifiers (AdaBoost, Random Forest, XGBoost, Logistic Regression)
• Select AdaBoost (≈86.9% accuracy)
• Serialize model, transformer, and encoder
• Build Flask backend with Gunicorn
• Create HTML/CSS/Bootstrap frontend with Jinja2 templates
• Integrate SHAP to generate feature‑contribution plots
• Add counseling booking and crisis assistance pages
• Containerize app with Docker
• Deploy to Render via GitHub Actions
• Set up automated uptime monitoring

证据

After tuning and evaluation, the AdaBoost classifier achieved approximately 86.9% accuracy and was selected as the final model.

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The application generates visual explanations showing how individual features contributed to a particular prediction.

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The project also contains serialized ML artifacts such as the trained model, preprocessing transformer and label encoder.

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