SmartHealthMonitoringSystem is a machine learning-powered health monitoring application designed to assess health risks based on user-provided data. This project leverages a Voting Classifier to predict risk levels and visualize key health metrics to assist users in monitoring their health proactively.
SmartHealthMonitoringSystem collects and analyzes health-related data, including metrics like heart rate, BMI, and stress levels, to predict the user's risk level. The app uses a Voting Classifier for a robust, ensemble-based classification.
- Predicts health risk levels based on input data.
- Visualizes health metrics like heart rate, calories burned, steps, and more.
- Supports a three-class classification: Low, Medium, and High risk.
- Built using synthetic health data to simulate real-world application.
The dataset synthetic_health_data.csv includes the following columns:
- user_id: Unique identifier for each user.
- age, gender, height_cm, weight_kg, bmi
- heart_rate, blood_pressure, calories_burned, steps
- sleep_hours, stress_level, risk_level
The Voting Classifier combines multiple machine learning models to improve predictive accuracy. The app uses:
- Decision Tree
- K-Nearest Neighbors (KNN)
- Random Forest
These models are combined to vote on the predicted risk level, achieving a more balanced classification outcome.
- Clone this repository:
git clone https://github.com/your-username/SmartHealthMonitoringSystem.git
- Install dependencies:
pip install -r requirements.txt
- Run the main app script:
python Health_App.py
- Enter your health data as prompted, or load a sample data file to test.
The Voting Classifier model achieves an accuracy of approximately 53% with the current dataset and configuration. Further tuning and data preprocessing may be applied to enhance model performance.
Contributions are welcome! Please fork this repository and create a pull request with your changes. For major updates, open an issue first to discuss potential enhancements.
This project is licensed under the MIT License. See the LICENSE file for details.