Substance Use in Mental Health Clients Classification
A classification model that predicts substance use disorders in mental health clients to enable early intervention and treatment planning.
Substance Use in Mental Health Clients Classification Model
This project develops a classification model to identify potential substance use disorders among mental health clients. Early identification enables timely intervention and integrated treatment approaches, significantly improving patient outcomes.
Key Features
- Multi-class Classification: Predicts the likelihood of different substance use disorders (alcohol, opioid, stimulant, etc.)
- Risk Stratification: Categorizes clients into risk levels for tailored intervention approaches
- Feature Importance Analysis: Identifies key clinical and demographic indicators of substance use comorbidity
- Privacy-Preserving Techniques: Implements data anonymization and security measures for sensitive healthcare data
- Model Explainability: Provides interpretable results for healthcare providers to understand predictions
Tech Stack
- Python
- Scikit-learn
- TensorFlow/Keras
- Pandas
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Flask API
Model Performance
The model achieved 87% accuracy in identifying substance use disorders, with particularly high performance (92% F1-score) for alcohol use disorders. The balanced approach prioritized both precision and recall to minimize both false positives and false negatives.
Clinical Applications
- Treatment Planning: Helps clinicians develop integrated treatment plans addressing both mental health and substance use
- Resource Allocation: Enables mental health facilities to allocate specialized resources to high-risk patients
- Early Intervention: Facilitates preventive interventions before substance use progresses to severe disorders
- Outcome Tracking: Monitors treatment effectiveness and adjusts approaches based on patient progress
Ethical Considerations
The project carefully addressed ethical concerns including bias mitigation, fair representation across demographic groups, and transparent decision support rather than automated diagnosis. The model serves as a clinical decision support tool, with final assessment remaining with qualified healthcare professionals.