Welcome to SEHAT, an interactive Predictive Health Application designed to forecast the likelihood of diabetes and heart disease. Utilizing Streamlit and pre-trained machine learning models, this application provides real-time predictions to empower users with valuable health insights.
SEHAT is a user-friendly web application that offers predictive insights into two major health concerns:
- Diabetes Prediction
- Heart Disease Prediction
Each section allows users to input specific health parameters, which are then processed by pre-trained machine learning models to provide immediate predictions.
You can find the repository here.
Users can input the following health parameters:
- Number of pregnancies
- Glucose level
- Blood pressure
- Skin thickness
- Insulin level
- BMI
- Diabetes pedigree function
- Age
The application uses a pre-loaded diabetes prediction model to process these inputs and generate a prediction on the potential presence or absence of diabetes.
Users can input the following medical attributes:
- Age
- Sex
- Chest pain type
- Resting blood pressure
- Serum cholesterol level
- Fasting blood sugar
- Electrocardiographic results
- Maximum heart rate achieved
- Exercise-induced angina
- ST depression induced by exercise
- Slope of the peak exercise ST segment
- Major vessels colored by fluoroscopy
- Thalassemia condition
The application uses a pre-trained heart disease prediction model to assess the likelihood of heart disease based on the provided details.
- Real-Time Predictions: Immediate insights into potential health conditions.
- User-Friendly Interface: Easy-to-use interface accommodating users from diverse backgrounds.
- Interactive Input Forms: Seamlessly input health parameters and trigger predictions with intuitive buttons.
- Educational Insights: Understand the correlation between health metrics and the likelihood of developing diabetes or heart disease.
Create an intuitive and easy-to-use interface accessible to individuals from diverse backgrounds and varying levels of technical proficiency.
Facilitate rapid health risk assessments through predictive models, assisting users in understanding their risk factors and encouraging them to seek professional medical advice.
Foster a proactive approach to healthcare by promoting early awareness and intervention. Encourage users to take informed steps towards managing their health.
Enhance users' understanding of how their health metrics correlate with the risk of developing diabetes or heart disease, fostering a greater sense of personal responsibility for health maintenance.
Emphasize the ethical and responsible use of predictive technology. Interpret predictive outcomes as indicators rather than conclusive diagnoses, highlighting the necessity of consulting healthcare professionals.
- Python 3.x
- Streamlit
- Pre-trained machine learning models for diabetes and heart disease prediction
- Clone the repository:
git clone https://github.com/MITTALBHAVYA/SEHAT-miniProject-sem5-
- Navigate to the project directory:
cd SEHAT - Install the required dependencies:
pip install -r requirements.txt
Start the Streamlit application:
streamlit run app.pyIf you'd like to contribute to this project, feel free to fork the repository and submit a pull request with your improvements or additional features.
This project is open-source and available under the MIT License.
I would like to thank my mentors, colleagues, and the online health and machine learning communities for their support and resources throughout the development of this project.
This README file provides an overview of the SEHAT project, detailing its contents, features, and how to get started with the application. SEHAT aims to be a catalyst for informed decision-making, promoting proactive health management, and contributing to improved health outcomes.