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
-
Clone the repository:
git clone https://github.com/Rakshitgupta9/Health-Companion.git
-
Navigate to the project directory:
cd Health-Companion
-
Install the required packages:
pip install -r requirements.txt
-
Set up the database:
- Import the SQL files located in the
database
folder into your MySQL database. - Update the database connection details in the
app.py
file.
- 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"