Fall Risk Assessment System
ML & AIAI-powered elderly care system predicting fall risk in next 6 months using machine learning (85.6% AUC). Interactive dashboard with real-time assessments and personalized recommendations.
Overview
💡 Challenge
Falls are the leading cause of injury-related deaths among elderly people (65+). Healthcare providers struggle to identify high-risk patients early, and manual risk assessments are time-consuming and inconsistent.
⚡ Solution
Developed a machine learning system using Logistic Regression analyzing 20+ clinical factors (mobility, medications, fall history, environmental hazards) to predict fall probability. Built premium interactive Streamlit dashboard with real-time risk assessment, animated visualizations, and evidence-based intervention recommendations.
🎯 Impact
Enables preventive care targeting with 79.5% accuracy. Early identification can prevent falls costing $50,000+ per incident. System provides immediate risk stratification (Low/Medium/High) with personalized action plans and timelines.
Technical Details
🛠️ Tech Stack
✨ Key Features
- ✓Logistic Regression model with 79.5% accuracy and 85.6% AUC score
- ✓80.8% sensitivity (catches 81% of people who will fall)
- ✓77.1% specificity (correctly identifies non-fallers)
- ✓Real-time fall risk prediction analyzing 20+ clinical factors
- ✓Interactive patient assessment with live feedback and warnings
- ✓Animated risk visualization with gauge charts and progress bars
- ✓Personalized intervention recommendations with detailed timelines
- ✓Risk factor importance analysis and contribution breakdown
- ✓Interactive risk simulator for testing "what-if" scenarios
- ✓Population analytics with charts showing risk distributions
- ✓Batch processing capability for multiple patients
- ✓Downloadable CSV assessment reports with full patient data
- ✓Premium UI with animations, gradients, and professional medical design
- ✓Mobile-responsive dashboard with sidebar statistics
Key Learnings
- →Synthetic healthcare data must have realistic correlations between risk factors for accurate model training
- →Logistic Regression chosen over Random Forest for better interpretability in clinical settings
- →Feature importance visualization helps clinicians understand and trust AI predictions
- →Interactive dashboards with live feedback improve user experience and data quality
- →Healthcare ML applications require clear communication of uncertainty and confidence levels
- →Model explainability is critical for clinical adoption and stakeholder buy-in
- →Premium UI/UX with animations significantly increases user engagement and satisfaction
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.