Predict, Prevent, Protect.
Empowering individuals to manage chronic conditions with personalized health insights. Begin your journey toward better health today.
-
Health Predictions
Leverage machine learning to predict potential health risks—anticipate and act before disaster strikes. It's proactive care, made simple. -
Real-time Monitoring
Imagine a personal health assistant tracking your vitals, 24/7. Instant feedback on your daily metrics for continuous optimization. -
Data Security
Rest assured, your health data is encrypted with military-grade security. Only authorized personnel have access to your information.
| Feature | Description |
|---|---|
| Health Score | Dynamically generated based on recent metrics to give you an at-a-glance health status. No data available yet. Start adding metrics. |
| Risk Level | Real-time assessment of your current risk for various conditions based on tracked data. No data available yet. |
| Next Check-up | Set reminders and manage your check-up schedule seamlessly. Feature coming soon. |
| Health Predictions | Your health metrics will directly impact your personalized health predictions. Add metrics to begin. |
| Recent Metrics | Quickly view the latest health metrics you've entered. No metrics available yet. Start tracking today. |
| Add New Health Metric | Easily track various health metrics including blood glucose, blood pressure, heart rate, and more. Add your first metric now. |
| Category | Technologies / Tools |
|---|---|
| Frontend | React.js, Next.js (14.2.18) |
| Backend & Security | Node.js, Express.js, reCAPTCHA, HSTS, Firebase |
| UI/UX Frameworks | Tailwind CSS, Radix UI, shadcn/ui, Lucide (icons) |
| Build Tools | Webpack, Vercel (deployment platform) |
| Data Handling | WebSockets (for real-time updates), GraphQL (data querying) |
| Performance | Priority Hints (performance optimization) |
| Machine Learning | TensorFlow, Firebase ML, Scikit-learn |
- Reminders for Check-ups and Medications
Automated reminders to ensure timely follow-ups and medication adherence. - Multi-device Health Data Sync
Sync your health data across multiple devices seamlessly for a unified experience. - Advanced Analytics Dashboard
Visualize and analyze trends in your health metrics with advanced analytics and predictive graphs. - AI-driven Health Insights
Receive tailored recommendations based on predictive models for a proactive health management strategy.
Explore the live version of the app here:
proofly
Install Proofly using pip:
pip install proofly- Python 3.8 or higher
- pip package manager
- Virtual environment (recommended)
from proofly import HealthAnalyzer
from proofly.models import DiabetesMetrics
from datetime import datetime
# Initialize the analyzer with custom configuration
analyzer = HealthAnalyzer(
config={
"logging_level": "INFO",
"cache_enabled": True,
"validation_mode": "strict"
}
)
# Create metrics using the type-safe model
metrics = DiabetesMetrics(
blood_glucose=120,
hba1c=6.5,
blood_pressure=130,
timestamp=datetime.now()
)
# Analyze metrics and get predictions
prediction = analyzer.predict_risk(metrics)
print(f"Risk Level: {prediction.risk_level}")
print(f"Confidence Score: {prediction.confidence}")
# Get personalized recommendations
recommendations = analyzer.get_recommendations(metrics)
for rec in recommendations:
print(f"- {rec.description}")Main analysis engine for processing health metrics and generating insights.
class HealthAnalyzer:
def predict_risk(self, metrics: BaseMetrics) -> AnalysisResult:
"""Predict health risks based on provided metrics."""
def get_recommendations(self, metrics: BaseMetrics) -> List[Recommendation]:
"""Generate personalized health recommendations."""Configuration management for analysis settings.
class MetricConfig:
"""Handles analyzer configuration and validation settings."""Container for analysis results and predictions.
class AnalysisResult:
"""Stores and manages analysis outcomes."""Utility for generating health reports and summaries.
class ReportGenerator:
"""Creates detailed health reports from analysis results."""- DiabetesMetrics: Blood glucose, HbA1c, blood pressure monitoring
- HypertensionMetrics: Blood pressure and heart rate tracking
- COPDMetrics: Respiratory function and symptoms
- CKDMetrics: Kidney function markers
- CHFMetrics: Heart failure indicators
class TrendAnalyzer:
"""Analyzes temporal patterns in health metrics."""class RiskCalculator:
"""Calculates health risk scores and probabilities."""class RecommendationEngine:
"""Generates personalized health recommendations."""class DataValidator:
"""Validates and sanitizes input health data."""config = {
"logging_level": "DEBUG", # DEBUG, INFO, WARNING, ERROR
"cache_enabled": True, # Enable result caching
"cache_ttl": 3600, # Cache timeout in seconds
"validation_mode": "strict", # strict or lenient
"prediction_threshold": 0.85, # Confidence threshold
"api_timeout": 30 # API request timeout in seconds
}
analyzer = HealthAnalyzer(config=config)from proofly.exceptions import ValidationError, AnalysisError
try:
metrics = DiabetesMetrics(blood_glucose=500) # Invalid value
except ValidationError as e:
print(f"Validation failed: {e}")
try:
prediction = analyzer.predict_risk(metrics)
except AnalysisError as e:
print(f"Analysis failed: {e}")| Python Version | Proofly Version | Support Status |
|---|---|---|
| 3.8 | 1.0.0+ | Supported |
| 3.9 | 1.0.0+ | Supported |
| 3.10 | 1.1.0+ | Supported |
| 3.11 | 1.1.2+ | Supported |
This is a personal project. If you have suggestions or would like to contribute, feel free to open an issue or submit a pull request.
For complete documentation, visit: proofly documentation
- Issues: GitHub Issues
- Email: Contact Us Directly
MIT License - see LICENSE for details.