Welcome to the Health Companion repository! This project aims to provide a comprehensive health monitoring and prediction tool using machine learning. The application can predict the risk of various diseases such as stroke, cardiovascular diseases, and diabetes. It also includes a BMI calculator and a calorie calculator.
- Introduction
- Features
- Technologies Used
- Installation
- Usage
- Database Schema
- Screenshots
- Future Enhancements
- Contributing
- License
Health Companion is a web application designed to help users monitor and predict their risk for certain health conditions using machine learning algorithms. The application is user-friendly and provides detailed information and insights based on user input.
- Disease Prediction: Predicts the risk of stroke, cardiovascular diseases, and diabetes.
- BMI Calculator: Calculates Body Mass Index (BMI) based on height and weight.
- Calorie Calculator: Estimates daily calorie needs based on various factors.
- User Authentication: Secure login and registration for personalized experience.
- Frontend: HTML, CSS, JavaScript
- Backend: Python (Flask)
- Database: MySQL
- Machine Learning: Various ML models for disease prediction
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Clone the repository:
git clone https://github.com/Rakshitgupta9/Health-Companion.git
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Navigate to the project directory:
cd Health-Companion -
Install the required packages:
pip install -r requirements.txt
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Set up the database:
- Import the SQL files located in the
databasefolder into your MySQL database. - Update the database connection details in the
app.pyfile.
- Import the SQL files located in the
-
Run the application:
python app.py
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Login: Access the application by logging in with your credentials. If you don't have an account, you can register a new one.
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Register: Create a new account by providing the necessary information.
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Dashboard: Once logged in, you can navigate to various features such as stroke risk prediction, cardiovascular disease prediction, diabetes risk prediction, BMI calculator, and calorie calculator.
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Disease Prediction: Enter the required information to get a prediction for the risk of stroke, cardiovascular disease, or diabetes.
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Results: View the prediction results along with additional insights and suggestions.
| Column | Type | Description |
|---|---|---|
| id | INT | Primary Key |
| age1 | INT | Age |
| gender1 | INT | Gender |
| height | FLOAT | Height |
| weight | FLOAT | Weight |
| ap_hi | INT | Systolic Blood Pressure |
| ap_lo | INT | Diastolic Blood Pressure |
| cholesterol | INT | Cholesterol Level |
| glu | INT | Glucose Level |
| smoke | INT | Smoking Status |
| alco | INT | Alcohol Intake |
| active | INT | Physical Activity |
| CARDIO_DISEASE | INT | Cardiovascular Disease Risk |
| Column | Type | Description |
|---|---|---|
| id | INT | Primary Key |
| pregnancies | INT | Number of Pregnancies |
| glucose | INT | Glucose Level |
| bloodpressure | INT | Blood Pressure |
| skinthickness | INT | Skin Thickness |
| insulin | INT | Insulin Level |
| bmi_dia | FLOAT | BMI |
| diabetes_pedigree_fnc | FLOAT | Diabetes Pedigree Function |
| age_dia | INT | Age |
| outcome | INT | Diabetes Risk |
| Column | Type | Description |
|---|---|---|
| id | INT | Primary Key |
| username | VARCHAR(50) | Username |
| password | VARCHAR(255) | Password |
| VARCHAR(100) |
| Column | Type | Description |
|---|---|---|
| id | INT | Primary Key |
| gender | INT | Gender |
| age | INT | Age |
| hypertension | INT | Hypertension Status |
| heart_disease | INT | Heart Disease Status |
| ever_married | INT | Marital Status |
| work_type | INT | Type of Work |
| residence_type | INT | Type of Residence |
| avg_glucose_level | FLOAT | Average Glucose Level |
| bmi | FLOAT | BMI |
| smoking_status | INT | Smoking Status |
| stroke | INT | Stroke Risk |
- Doctor Information: Provide information about doctors near the user's location for specific diseases.
- Remedies and Tips: Offer remedies and health tips based on the user's health data.
- More Disease Predictions: Expand the application to predict risks for additional diseases.
Contributions are welcome!
This project is licensed under the MIT License. See the LICENSE file for details. "# HealthCompanion" "# HealthCompanion"