AI-Powered Cardiovascular Risk Prediction Platform built using Machine Learning, Flask, and Interactive Health Analytics.
CardioRisk Analytics is a full-stack healthcare analytics platform that predicts cardiovascular disease risk using machine learning models trained on the CDC BRFSS 2021 dataset.
The platform combines predictive modeling, health analytics, risk visualization, and personalized recommendations through an interactive web dashboard.
- Cardiovascular Disease Risk Prediction
- Real-Time Health Analytics Dashboard
- Automatic BMI Calculation
- Interactive Risk Gauge
- Personalized Health Recommendations
- Random Forest Prediction Model
- Logistic Regression Prediction Model
- Neural Network Prediction Model
- Model Performance Comparison
- Responsive User Interface
CDC BRFSS 2021 Dataset
- 308,000+ Health Records
- Multiple Lifestyle Indicators
- Cardiovascular Disease Labels
Source:
https://www.kaggle.com/datasets/alphiree/cardiovascular-diseases-risk-prediction-dataset
Pre-trained model files are not included in this repository due to GitHub file size limitations.
To generate the models locally:
python train_models.pyGenerated model files will be stored in the models/ directory.
Models Used:
- Random Forest Classifier
- Logistic Regression
- Neural Network (MLP)
- Python
- Flask
- Scikit-Learn
- Pandas
- NumPy
- Joblib
- HTML
- CSS
- JavaScript
- Chart.js
- Random Forest
- Logistic Regression
- Neural Network (MLP)
User Input
↓
Data Validation
↓
Feature Processing
↓
Machine Learning Models
↓
Risk Prediction
↓
Health Analytics Dashboard
git clone https://github.com/thisissaditya/CardioRisk-Analytics.git
cd CardioRisk-Analytics
pip install -r requirements.txt
python train_models.py
python app.py- Explainable AI (SHAP)
- User Authentication
- Medical Report Generation
- Cloud Deployment
- Mobile Application
Purshottam Rakesh
B.Tech Computer Science Engineering (AI & ML)
VIT Bhopal University
GitHub: https://github.com/thisissaditya
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